Instantaneous network model diagnostics - balanced statistics
This file shows diagnostics for instantaneous network models fit using balanced racial/ethnic mixing matrices and degree terms adjusted to correspond to the balanced mixing matrices. In this file, we fit a series of nested models by adding one term at a time to examine changes to model estimates, MCMC diagnostics, and network diagnostics.
Load packages and model fits
rm(list = ls())
suppressMessages(library("EpiModelHIV"))
library("latticeExtra")## Loading required package: lattice## Loading required package: RColorBrewerlibrary("knitr")
library("kableExtra")
load(file = "/homes/dpwhite/R/GitHub Repos/WHAMP/Model fits and simulations/Fit tests and debugging/est/fit.i.buildup.bal.rda")Model terms and control settings
| Terms | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 |
|---|---|---|---|---|---|---|---|---|
| edges | 479.2 | 479.2 | 479.2 | 479.2 | 479.2 | 479.2 | 479.2 | 479.2 |
| nodefactor.deg.main.deg.pers.0.1 | NA | NA | NA | 172.3 | 172.3 | 172.3 | 172.3 | 172.3 |
| nodefactor.deg.main.deg.pers.0.2 | NA | NA | NA | 36.4 | 36.4 | 36.4 | 36.4 | 36.4 |
| nodefactor.deg.main.deg.pers.1.0 | NA | NA | NA | 38.0 | 38.0 | 38.0 | 38.0 | 38.0 |
| nodefactor.deg.main.deg.pers.1.1 | NA | NA | NA | 135.5 | 135.5 | 135.5 | 135.5 | 135.5 |
| nodefactor.deg.main.deg.pers.1.2 | NA | NA | NA | 145.4 | 145.4 | 145.4 | 145.4 | 145.4 |
| nodefactor.riskg.O1 | NA | NA | NA | NA | NA | NA | 0.0 | 0.0 |
| nodefactor.riskg.O2 | NA | NA | NA | NA | NA | NA | 0.0 | 0.0 |
| nodefactor.riskg.O3 | NA | NA | NA | NA | NA | NA | 6.9 | 6.9 |
| nodefactor.riskg.O4 | NA | NA | NA | NA | NA | NA | 109.5 | 109.5 |
| nodefactor.riskg.Y1 | NA | NA | NA | NA | NA | NA | 0.0 | 0.0 |
| nodefactor.riskg.Y2 | NA | NA | NA | NA | NA | NA | 8.2 | 8.2 |
| nodefactor.riskg.Y3 | NA | NA | NA | NA | NA | NA | 70.8 | 70.8 |
| nodefactor.race..wa.B | NA | 75.6 | 75.6 | 75.6 | 75.6 | 75.6 | 75.6 | 75.6 |
| nodefactor.race..wa.H | NA | 149.2 | 149.2 | 149.2 | 149.2 | 149.2 | 149.2 | 149.2 |
| nodefactor.region.EW | NA | NA | NA | NA | 83.5 | 83.5 | 83.5 | 83.5 |
| nodefactor.region.OW | NA | NA | NA | NA | 242.5 | 242.5 | 242.5 | 242.5 |
| nodematch.race..wa.B | NA | NA | 2.5 | 2.5 | 2.5 | 2.5 | 2.5 | 2.5 |
| nodematch.race..wa.H | NA | NA | 13.3 | 13.3 | 13.3 | 13.3 | 13.3 | 13.3 |
| nodematch.race..wa.O | NA | NA | 286.9 | 286.9 | 286.9 | 286.9 | 286.9 | 286.9 |
| nodematch.region | NA | NA | NA | NA | NA | NA | NA | 383.3 |
| absdiff.sqrt.age | NA | NA | NA | NA | NA | 380.5 | 380.5 | 380.5 |
| nodematch.role.class.I | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf |
| nodematch.role.class.R | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf |
The control settings for these models are:
set.control.ergm = control.ergm(MCMC.interval = 1e+5,
MCMC.samplesize = 7500,
MCMC.burnin = 1e+6,
MPLE.max.dyad.types = 1e+7,
MCMLE.maxit = 400,
parallel = np/2,
parallel.type="PSOCK"))MCMC diagnostics
Model 1
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## 1.8838 21.6899 0.1252 0.1239
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## -40.159 -13.159 1.841 16.841 44.841
##
##
## Sample statistics cross-correlations:
## edges
## edges 1
##
## Sample statistics auto-correlation:
## Chain 1
## edges
## Lag 0 1.000000000
## Lag 1e+05 0.021332915
## Lag 2e+05 0.002669399
## Lag 3e+05 -0.004888273
## Lag 4e+05 0.011901587
## Lag 5e+05 0.011800576
## Chain 2
## edges
## Lag 0 1.000000000
## Lag 1e+05 0.013242592
## Lag 2e+05 0.016382186
## Lag 3e+05 -0.020064087
## Lag 4e+05 0.002296841
## Lag 5e+05 0.009784449
## Chain 3
## edges
## Lag 0 1.0000000000
## Lag 1e+05 -0.0091597100
## Lag 2e+05 -0.0005450508
## Lag 3e+05 -0.0119443121
## Lag 4e+05 -0.0252038444
## Lag 5e+05 -0.0465869881
## Chain 4
## edges
## Lag 0 1.0000000000
## Lag 1e+05 -0.0194879913
## Lag 2e+05 0.0157836779
## Lag 3e+05 -0.0235470441
## Lag 4e+05 -0.0283273184
## Lag 5e+05 0.0005466337
## Chain 5
## edges
## Lag 0 1.000000000
## Lag 1e+05 0.008267646
## Lag 2e+05 -0.004205927
## Lag 3e+05 0.011081478
## Lag 4e+05 0.005215142
## Lag 5e+05 0.002653280
## Chain 6
## edges
## Lag 0 1.000000000
## Lag 1e+05 0.000403967
## Lag 2e+05 -0.019047133
## Lag 3e+05 0.028459673
## Lag 4e+05 0.021563362
## Lag 5e+05 0.005491952
## Chain 7
## edges
## Lag 0 1.000000000
## Lag 1e+05 0.005402465
## Lag 2e+05 -0.008740617
## Lag 3e+05 0.020281924
## Lag 4e+05 -0.021406586
## Lag 5e+05 -0.006723432
## Chain 8
## edges
## Lag 0 1.0000000000
## Lag 1e+05 -0.0056448333
## Lag 2e+05 0.0066092961
## Lag 3e+05 -0.0005693126
## Lag 4e+05 0.0257470120
## Lag 5e+05 -0.0035451231
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.3158
##
## Individual P-values (lower = worse):
## edges
## 0.7521337
## Joint P-value (lower = worse): 0.7481276 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.163
##
## Individual P-values (lower = worse):
## edges
## 0.870489
## Joint P-value (lower = worse): 0.8762538 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.7798
##
## Individual P-values (lower = worse):
## edges
## 0.4354886
## Joint P-value (lower = worse): 0.3953077 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -0.3549
##
## Individual P-values (lower = worse):
## edges
## 0.722691
## Joint P-value (lower = worse): 0.7295304 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.6891
##
## Individual P-values (lower = worse):
## edges
## 0.4907354
## Joint P-value (lower = worse): 0.416782 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.9951
##
## Individual P-values (lower = worse):
## edges
## 0.3196815
## Joint P-value (lower = worse): 0.3223314 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.205
##
## Individual P-values (lower = worse):
## edges
## 0.8376064
## Joint P-value (lower = worse): 0.8382153 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.5326
##
## Individual P-values (lower = worse):
## edges
## 0.5942949
## Joint P-value (lower = worse): 0.5886258 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 2
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 0.83031 21.851 0.12615 0.12557
## nodefactor.race..wa.B -0.07032 9.057 0.05229 0.05221
## nodefactor.race..wa.H -0.27285 13.152 0.07593 0.07617
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -42.16 -14.159 0.8414 15.841 43.84
## nodefactor.race..wa.B -17.59 -6.591 -0.5908 5.409 18.41
## nodefactor.race..wa.H -25.17 -9.174 -0.1739 8.826 25.83
##
##
## Sample statistics cross-correlations:
## edges nodefactor.race..wa.B
## edges 1.0000000 0.3822744
## nodefactor.race..wa.B 0.3822744 1.0000000
## nodefactor.race..wa.H 0.5184546 0.1044710
## nodefactor.race..wa.H
## edges 0.5184546
## nodefactor.race..wa.B 0.1044710
## nodefactor.race..wa.H 1.0000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.001010140 -0.019467684 -0.0091849257
## Lag 2e+05 -0.013309993 0.035413701 0.0081303469
## Lag 3e+05 -0.012150594 -0.028372853 -0.0063887810
## Lag 4e+05 0.001022307 0.009074304 0.0103598879
## Lag 5e+05 -0.007844441 -0.002531519 0.0006497536
## Chain 2
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.012339381 -0.001678819 0.0241005868
## Lag 2e+05 -0.012337337 -0.020303192 -0.0147918322
## Lag 3e+05 -0.017094795 -0.016690382 0.0388980665
## Lag 4e+05 0.014955208 -0.024040245 -0.0077695262
## Lag 5e+05 -0.008166303 -0.019730742 0.0006704216
## Chain 3
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.034933666 -0.019433867 -0.013702963
## Lag 2e+05 -0.013711447 -0.002375690 -0.002330164
## Lag 3e+05 0.036875720 0.030341706 0.002544738
## Lag 4e+05 -0.013010745 0.004498821 -0.042817338
## Lag 5e+05 -0.006653495 -0.009852775 0.019762354
## Chain 4
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 -0.025560287 0.019179924 -0.01963993
## Lag 2e+05 -0.002606629 0.001631863 0.01164375
## Lag 3e+05 -0.012289035 0.017207930 0.00236245
## Lag 4e+05 0.013203355 -0.010313224 -0.01749572
## Lag 5e+05 0.012393214 -0.014146107 0.02006803
## Chain 5
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0009054197 -0.005400592 -0.019376493
## Lag 2e+05 -0.0256854346 -0.011866056 -0.021833522
## Lag 3e+05 0.0050058580 -0.008008414 0.010399116
## Lag 4e+05 0.0274453228 -0.012414612 -0.010899842
## Lag 5e+05 0.0025467303 0.005753679 -0.006187236
## Chain 6
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.019364377 -0.012750006 0.0110296874
## Lag 2e+05 -0.005764475 -0.027455507 0.0006239673
## Lag 3e+05 0.017347967 0.003393639 0.0132321771
## Lag 4e+05 0.000884350 -0.003054325 -0.0137799476
## Lag 5e+05 -0.022301216 0.015052424 0.0142459134
## Chain 7
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.016990353 -0.008017949 -0.0009086738
## Lag 2e+05 -0.003040907 0.024137173 0.0088298218
## Lag 3e+05 -0.006717174 0.007567443 -0.0023874087
## Lag 4e+05 0.025791851 -0.021078755 -0.0010915776
## Lag 5e+05 -0.009758205 0.014931933 -0.0059506438
## Chain 8
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.003702920 -0.005928410 -0.027147198
## Lag 2e+05 0.018761786 -0.004274763 -0.015198987
## Lag 3e+05 -0.007136121 0.003303904 -0.015079505
## Lag 4e+05 -0.016247065 -0.003356730 -0.024898982
## Lag 5e+05 0.016601059 0.019499094 0.005399776
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.2840 0.7097 -0.7415
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.7764453 0.4778733 0.4584055
## Joint P-value (lower = worse): 0.7493245 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -2.8411 -0.6001 -2.0372
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.004496186 0.548455431 0.041633559
## Joint P-value (lower = worse): 0.02990708 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.5278 2.6987 0.3133
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.126564007 0.006961244 0.754027049
## Joint P-value (lower = worse): 0.02374097 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.2893 0.1263 0.8609
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.7723283 0.8994802 0.3892846
## Joint P-value (lower = worse): 0.8499357 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.6673 -0.3933 1.0758
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.5045959 0.6940699 0.2820053
## Joint P-value (lower = worse): 0.7086603 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.1434 -0.7948 0.9533
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.8860027 0.4267398 0.3404347
## Joint P-value (lower = worse): 0.6423003 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.7383 0.1633 0.8393
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4603143 0.8702701 0.4012915
## Joint P-value (lower = worse): 0.8497453 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -1.28947 0.23874 -0.05555
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.1972337 0.8113058 0.9557018
## Joint P-value (lower = worse): 0.3748216 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 3
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 0.69291 21.878 0.126311 0.128010
## nodefactor.race..wa.B 0.29362 9.047 0.052231 0.052081
## nodefactor.race..wa.H -0.40912 13.346 0.077053 0.076049
## nodematch.race..wa.B 0.02565 1.594 0.009204 0.009212
## nodematch.race..wa.H 0.02298 3.650 0.021074 0.021076
## nodematch.race..wa.O 0.82294 16.908 0.097619 0.097303
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -41.159 -14.159 0.8414 14.841 43.841
## nodefactor.race..wa.B -16.591 -5.591 0.4092 6.409 18.409
## nodefactor.race..wa.H -26.174 -9.174 -0.1739 8.826 25.826
## nodematch.race..wa.B -2.540 -1.540 -0.5399 1.460 3.460
## nodematch.race..wa.H -6.269 -2.269 -0.2690 2.731 7.731
## nodematch.race..wa.O -31.880 -10.880 1.1200 12.120 35.120
##
##
## Sample statistics cross-correlations:
## edges nodefactor.race..wa.B
## edges 1.00000000 0.386569258
## nodefactor.race..wa.B 0.38656926 1.000000000
## nodefactor.race..wa.H 0.51581135 0.145342125
## nodematch.race..wa.B 0.07995475 0.355558325
## nodematch.race..wa.H 0.16779658 0.007270553
## nodematch.race..wa.O 0.77036083 -0.003145322
## nodefactor.race..wa.H nodematch.race..wa.B
## edges 0.515811349 0.079954750
## nodefactor.race..wa.B 0.145342125 0.355558325
## nodefactor.race..wa.H 1.000000000 -0.001411331
## nodematch.race..wa.B -0.001411331 1.000000000
## nodematch.race..wa.H 0.554030980 0.004486455
## nodematch.race..wa.O -0.003541365 0.009798063
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.167796584 0.770360833
## nodefactor.race..wa.B 0.007270553 -0.003145322
## nodefactor.race..wa.H 0.554030980 -0.003541365
## nodematch.race..wa.B 0.004486455 0.009798063
## nodematch.race..wa.H 1.000000000 -0.006426807
## nodematch.race..wa.O -0.006426807 1.000000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.0000000000
## Lag 1e+05 -0.022675772 0.0006146616 -0.0083120881
## Lag 2e+05 -0.014077697 0.0154202110 -0.0055351461
## Lag 3e+05 -0.006272102 -0.0171592046 -0.0254126728
## Lag 4e+05 -0.013327612 0.0094650800 -0.0007091522
## Lag 5e+05 -0.024821989 0.0104860126 -0.0148144489
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.0000000000 1.0000000000
## Lag 1e+05 0.032045465 -0.0151649417 -0.0220772699
## Lag 2e+05 0.002226521 -0.0255652509 -0.0093373496
## Lag 3e+05 -0.014934430 0.0001332612 0.0003144278
## Lag 4e+05 -0.026353775 -0.0044141050 -0.0239276402
## Lag 5e+05 0.007644851 -0.0089063489 -0.0333204834
## Chain 2
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.029121169 -0.010907475 0.0080886913
## Lag 2e+05 0.005738136 -0.004865801 -0.0003358323
## Lag 3e+05 -0.006147485 0.026129451 0.0052815298
## Lag 4e+05 -0.003800107 0.028194188 0.0305087256
## Lag 5e+05 0.007590824 -0.018798471 -0.0155963611
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.005809010 -0.003737416 0.0046228204
## Lag 2e+05 0.001707830 -0.005358989 0.0169308866
## Lag 3e+05 0.016406659 -0.021736431 0.0049404980
## Lag 4e+05 -0.006844874 0.010828660 -0.0164817799
## Lag 5e+05 -0.008744926 -0.015683156 -0.0008358697
## Chain 3
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.008874101 -0.011398018 0.0005693972
## Lag 2e+05 -0.021902032 0.010700622 -0.0284040512
## Lag 3e+05 -0.002643022 -0.013065788 0.0097565780
## Lag 4e+05 0.001771536 -0.004795417 0.0105749138
## Lag 5e+05 -0.004393681 0.008282554 0.0203708763
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000e+00
## Lag 1e+05 0.017856540 0.006004845 1.932043e-02
## Lag 2e+05 0.018154177 -0.003368495 -1.336832e-02
## Lag 3e+05 0.004485060 -0.004835573 1.171974e-02
## Lag 4e+05 0.006893821 0.011537571 2.630285e-05
## Lag 5e+05 -0.002408604 0.015793488 -1.109697e-02
## Chain 4
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.035174112 0.016053051 0.021770878
## Lag 2e+05 0.005500856 -0.006241258 -0.007629545
## Lag 3e+05 -0.011271854 0.008741685 -0.006536236
## Lag 4e+05 0.008628064 0.011392173 0.005035628
## Lag 5e+05 0.011434777 0.005337484 -0.005130564
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.003234300 -0.011933744 0.0284146164
## Lag 2e+05 0.017097113 -0.028046341 0.0006783752
## Lag 3e+05 -0.026186738 0.017362015 0.0090995487
## Lag 4e+05 -0.004638035 0.015549463 -0.0083709478
## Lag 5e+05 -0.015393176 -0.003464642 0.0219351839
## Chain 5
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.001509637 0.004808405 -0.003267671
## Lag 2e+05 -0.001255768 -0.014005328 -0.024023341
## Lag 3e+05 0.019736424 -0.019741308 0.001502842
## Lag 4e+05 -0.025926108 0.005920689 -0.011575218
## Lag 5e+05 0.016661959 -0.007618715 0.009986901
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 -0.0138354969 0.001738005 -0.003628185
## Lag 2e+05 -0.0004870505 0.005157037 -0.003098918
## Lag 3e+05 0.0009086615 0.002116831 0.027899734
## Lag 4e+05 0.0277904495 0.004993505 -0.009911999
## Lag 5e+05 0.0025864668 -0.007109247 0.011832001
## Chain 6
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 -0.015206093 -0.023619279 -0.01280746
## Lag 2e+05 -0.017565203 0.022687978 0.01124768
## Lag 3e+05 0.006385590 0.017198537 -0.01137232
## Lag 4e+05 -0.004879522 0.004262916 -0.01146235
## Lag 5e+05 0.009011178 -0.013336363 -0.03271418
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.004963816 0.0004390113 -0.001395031
## Lag 2e+05 0.023874443 0.0192964547 -0.006001586
## Lag 3e+05 -0.002378887 -0.0021653707 0.004827755
## Lag 4e+05 0.017723726 -0.0084445674 -0.007017396
## Lag 5e+05 -0.026981104 -0.0159064201 0.014468773
## Chain 7
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.017099436 0.017554840 0.004419538
## Lag 2e+05 -0.024718330 0.021528028 -0.004303942
## Lag 3e+05 0.002036409 -0.006411881 -0.015281415
## Lag 4e+05 -0.005575458 0.008045303 -0.003828651
## Lag 5e+05 -0.016234236 -0.009945874 -0.006132275
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.027030705 0.021045461 0.007545288
## Lag 2e+05 -0.012084245 0.007378371 -0.021859915
## Lag 3e+05 -0.001087241 -0.014811248 0.009740747
## Lag 4e+05 -0.010298133 0.017533198 -0.018320157
## Lag 5e+05 0.008083214 -0.013399691 -0.020915500
## Chain 8
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0030419127 -0.015513148 -0.014068116
## Lag 2e+05 -0.0001922772 -0.025769536 -0.026147045
## Lag 3e+05 0.0294109564 0.013958918 -0.015872966
## Lag 4e+05 0.0064599134 -0.003920452 0.003230989
## Lag 5e+05 -0.0182067593 -0.004678824 0.011809763
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 -0.0077018271 -0.010501068 -0.001814110
## Lag 2e+05 -0.0139263120 -0.016320287 -0.003825018
## Lag 3e+05 -0.0212615865 0.002717656 0.021652751
## Lag 4e+05 -0.0009990131 0.008444423 0.026129735
## Lag 5e+05 -0.0145885672 0.029492799 -0.010239042
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.40691 -1.04381 0.95707
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -1.18518 -0.22772 -0.03952
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.6840749 0.2965734 0.3385307
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.2359481 0.8198654 0.9684730
## Joint P-value (lower = worse): 0.5839334 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.26809 0.17786 -0.57252
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -1.31455 -0.52116 -0.04544
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.7886295 0.8588299 0.5669687
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.1886603 0.6022565 0.9637589
## Joint P-value (lower = worse): 0.8112875 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.9027 0.1736 -0.4147
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 1.4932 0.4797 1.3789
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.3666969 0.8621617 0.6783294
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.1353837 0.6314568 0.1679244
## Joint P-value (lower = worse): 0.3998128 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.76301 0.04821 1.38389
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.27223 2.00927 1.66440
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.07789838 0.96155220 0.16639093
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.78544311 0.04450815 0.09603332
## Joint P-value (lower = worse): 0.3242515 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -1.6685 -0.1474 -1.6330
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 2.0298 -1.4886 -0.9866
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.09521441 0.88282989 0.10246827
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.04237977 0.13659792 0.32383529
## Joint P-value (lower = worse): 0.1572184 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -2.5351 -0.7746 -1.2012
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.7424 0.7332 -1.7707
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.01124122 0.43858239 0.22968342
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.45781676 0.46341156 0.07660621
## Joint P-value (lower = worse): 0.1112494 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.01107 -0.56650 0.13873
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 1.05242 1.08276 0.44641
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.9911684 0.5710521 0.8896612
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.2926062 0.2789159 0.6553009
## Joint P-value (lower = worse): 0.7038813 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.11380 0.31600 -0.42556
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.30663 0.04048 0.07267
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.9093993 0.7520026 0.6704274
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.7591267 0.9677090 0.9420691
## Joint P-value (lower = worse): 0.8136421 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 4
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 2.46385 21.838 0.12608 0.125042
## nodefactor.deg.main.deg.pers.0.1 1.24993 14.291 0.08251 0.082886
## nodefactor.deg.main.deg.pers.0.2 0.14720 6.107 0.03526 0.035273
## nodefactor.deg.main.deg.pers.1.0 0.15200 6.272 0.03621 0.036196
## nodefactor.deg.main.deg.pers.1.1 0.34030 12.431 0.07177 0.072081
## nodefactor.deg.main.deg.pers.1.2 0.94144 12.978 0.07493 0.075542
## nodefactor.race..wa.B 0.30892 9.007 0.05200 0.051818
## nodefactor.race..wa.H 0.76621 13.272 0.07662 0.077434
## nodematch.race..wa.B 0.03198 1.604 0.00926 0.008985
## nodematch.race..wa.H 0.05965 3.656 0.02111 0.021183
## nodematch.race..wa.O 1.46968 16.994 0.09812 0.098807
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -40.159 -12.159 1.84138 16.841 45.841
## nodefactor.deg.main.deg.pers.0.1 -26.310 -8.310 0.68996 10.690 29.690
## nodefactor.deg.main.deg.pers.0.2 -11.371 -4.371 -0.37103 4.629 12.629
## nodefactor.deg.main.deg.pers.1.0 -12.033 -4.033 -0.03347 3.967 12.967
## nodefactor.deg.main.deg.pers.1.1 -23.538 -8.538 0.46214 8.462 25.462
## nodefactor.deg.main.deg.pers.1.2 -23.388 -8.388 0.61188 9.612 26.612
## nodefactor.race..wa.B -16.591 -5.591 0.40918 6.409 18.409
## nodefactor.race..wa.H -24.174 -8.174 0.82608 9.826 27.826
## nodematch.race..wa.B -2.540 -1.540 -0.53985 1.460 3.460
## nodematch.race..wa.H -6.269 -2.269 -0.26902 2.731 7.731
## nodematch.race..wa.O -31.880 -9.880 1.11998 13.120 35.120
##
##
## Sample statistics cross-correlations:
## edges
## edges 1.00000000
## nodefactor.deg.main.deg.pers.0.1 0.55125656
## nodefactor.deg.main.deg.pers.0.2 0.25720137
## nodefactor.deg.main.deg.pers.1.0 0.26898698
## nodefactor.deg.main.deg.pers.1.1 0.50102638
## nodefactor.deg.main.deg.pers.1.2 0.51505485
## nodefactor.race..wa.B 0.38515303
## nodefactor.race..wa.H 0.50830123
## nodematch.race..wa.B 0.07625125
## nodematch.race..wa.H 0.16572928
## nodematch.race..wa.O 0.77214352
## nodefactor.deg.main.deg.pers.0.1
## edges 0.55125656
## nodefactor.deg.main.deg.pers.0.1 1.00000000
## nodefactor.deg.main.deg.pers.0.2 0.07549957
## nodefactor.deg.main.deg.pers.1.0 0.07800802
## nodefactor.deg.main.deg.pers.1.1 0.14349914
## nodefactor.deg.main.deg.pers.1.2 0.14051105
## nodefactor.race..wa.B 0.23541555
## nodefactor.race..wa.H 0.24070504
## nodematch.race..wa.B 0.04681900
## nodematch.race..wa.H 0.06428990
## nodematch.race..wa.O 0.43932145
## nodefactor.deg.main.deg.pers.0.2
## edges 0.25720137
## nodefactor.deg.main.deg.pers.0.1 0.07549957
## nodefactor.deg.main.deg.pers.0.2 1.00000000
## nodefactor.deg.main.deg.pers.1.0 0.02893853
## nodefactor.deg.main.deg.pers.1.1 0.05697431
## nodefactor.deg.main.deg.pers.1.2 0.06732021
## nodefactor.race..wa.B 0.12357105
## nodefactor.race..wa.H 0.12744806
## nodematch.race..wa.B 0.02040527
## nodematch.race..wa.H 0.04648005
## nodematch.race..wa.O 0.19172321
## nodefactor.deg.main.deg.pers.1.0
## edges 0.26898698
## nodefactor.deg.main.deg.pers.0.1 0.07800802
## nodefactor.deg.main.deg.pers.0.2 0.02893853
## nodefactor.deg.main.deg.pers.1.0 1.00000000
## nodefactor.deg.main.deg.pers.1.1 0.06693272
## nodefactor.deg.main.deg.pers.1.2 0.06835165
## nodefactor.race..wa.B 0.09197375
## nodefactor.race..wa.H 0.14801133
## nodematch.race..wa.B 0.02254600
## nodematch.race..wa.H 0.04012153
## nodematch.race..wa.O 0.20340052
## nodefactor.deg.main.deg.pers.1.1
## edges 0.50102638
## nodefactor.deg.main.deg.pers.0.1 0.14349914
## nodefactor.deg.main.deg.pers.0.2 0.05697431
## nodefactor.deg.main.deg.pers.1.0 0.06693272
## nodefactor.deg.main.deg.pers.1.1 1.00000000
## nodefactor.deg.main.deg.pers.1.2 0.13139593
## nodefactor.race..wa.B 0.16529749
## nodefactor.race..wa.H 0.31725426
## nodematch.race..wa.B 0.02680594
## nodematch.race..wa.H 0.12033565
## nodematch.race..wa.O 0.36091677
## nodefactor.deg.main.deg.pers.1.2
## edges 0.51505485
## nodefactor.deg.main.deg.pers.0.1 0.14051105
## nodefactor.deg.main.deg.pers.0.2 0.06732021
## nodefactor.deg.main.deg.pers.1.0 0.06835165
## nodefactor.deg.main.deg.pers.1.1 0.13139593
## nodefactor.deg.main.deg.pers.1.2 1.00000000
## nodefactor.race..wa.B 0.15385775
## nodefactor.race..wa.H 0.30651739
## nodematch.race..wa.B 0.01908541
## nodematch.race..wa.H 0.11840396
## nodematch.race..wa.O 0.38960060
## nodefactor.race..wa.B
## edges 0.385153033
## nodefactor.deg.main.deg.pers.0.1 0.235415552
## nodefactor.deg.main.deg.pers.0.2 0.123571054
## nodefactor.deg.main.deg.pers.1.0 0.091973747
## nodefactor.deg.main.deg.pers.1.1 0.165297493
## nodefactor.deg.main.deg.pers.1.2 0.153857753
## nodefactor.race..wa.B 1.000000000
## nodefactor.race..wa.H 0.144556548
## nodematch.race..wa.B 0.364753667
## nodematch.race..wa.H 0.003960033
## nodematch.race..wa.O -0.003493389
## nodefactor.race..wa.H
## edges 0.508301228
## nodefactor.deg.main.deg.pers.0.1 0.240705044
## nodefactor.deg.main.deg.pers.0.2 0.127448061
## nodefactor.deg.main.deg.pers.1.0 0.148011335
## nodefactor.deg.main.deg.pers.1.1 0.317254263
## nodefactor.deg.main.deg.pers.1.2 0.306517394
## nodefactor.race..wa.B 0.144556548
## nodefactor.race..wa.H 1.000000000
## nodematch.race..wa.B -0.002287859
## nodematch.race..wa.H 0.554879545
## nodematch.race..wa.O -0.009496518
## nodematch.race..wa.B nodematch.race..wa.H
## edges 0.0762512500 0.1657292753
## nodefactor.deg.main.deg.pers.0.1 0.0468189967 0.0642898974
## nodefactor.deg.main.deg.pers.0.2 0.0204052716 0.0464800533
## nodefactor.deg.main.deg.pers.1.0 0.0225459979 0.0401215274
## nodefactor.deg.main.deg.pers.1.1 0.0268059419 0.1203356479
## nodefactor.deg.main.deg.pers.1.2 0.0190854085 0.1184039632
## nodefactor.race..wa.B 0.3647536668 0.0039600333
## nodefactor.race..wa.H -0.0022878586 0.5548795451
## nodematch.race..wa.B 1.0000000000 0.0009421418
## nodematch.race..wa.H 0.0009421418 1.0000000000
## nodematch.race..wa.O 0.0044648171 -0.0060942181
## nodematch.race..wa.O
## edges 0.772143520
## nodefactor.deg.main.deg.pers.0.1 0.439321446
## nodefactor.deg.main.deg.pers.0.2 0.191723211
## nodefactor.deg.main.deg.pers.1.0 0.203400524
## nodefactor.deg.main.deg.pers.1.1 0.360916766
## nodefactor.deg.main.deg.pers.1.2 0.389600605
## nodefactor.race..wa.B -0.003493389
## nodefactor.race..wa.H -0.009496518
## nodematch.race..wa.B 0.004464817
## nodematch.race..wa.H -0.006094218
## nodematch.race..wa.O 1.000000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.009329272 0.002137759
## Lag 2e+05 -0.016015374 0.010362244
## Lag 3e+05 0.005878918 -0.015122796
## Lag 4e+05 -0.021252614 -0.032391113
## Lag 5e+05 -0.008957245 -0.007523296
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.039681801
## Lag 2e+05 -0.006118591
## Lag 3e+05 0.012459735
## Lag 4e+05 0.003893667
## Lag 5e+05 -0.022528391
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.034991308
## Lag 2e+05 -0.002040746
## Lag 3e+05 -0.002617928
## Lag 4e+05 -0.017763451
## Lag 5e+05 0.022649614
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 -0.006045159
## Lag 2e+05 -0.012702262
## Lag 3e+05 0.009454976
## Lag 4e+05 -0.012766890
## Lag 5e+05 -0.005387883
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.008740181 0.010412885
## Lag 2e+05 0.005238224 -0.010344572
## Lag 3e+05 -0.024177340 -0.005024137
## Lag 4e+05 -0.029368597 -0.004591150
## Lag 5e+05 0.014245884 0.009156012
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.014110715 -0.028387101 -0.0025949456
## Lag 2e+05 0.031708991 -0.028422825 0.0113576031
## Lag 3e+05 -0.001721649 -0.009505605 -0.0004958602
## Lag 4e+05 -0.007612822 0.025185434 0.0068489320
## Lag 5e+05 -0.033151355 0.022337050 -0.0222616730
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.008705597
## Lag 2e+05 -0.026786019
## Lag 3e+05 0.008738070
## Lag 4e+05 -0.011501663
## Lag 5e+05 0.003558997
## Chain 2
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 0.0022029639 0.005241233
## Lag 2e+05 0.0085818113 0.010166303
## Lag 3e+05 0.0005313363 -0.016562740
## Lag 4e+05 -0.0186813029 0.016233984
## Lag 5e+05 -0.0220210648 -0.004449174
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.006695351
## Lag 2e+05 -0.011479548
## Lag 3e+05 -0.009264648
## Lag 4e+05 0.009918676
## Lag 5e+05 -0.012731469
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.00000000
## Lag 1e+05 0.01239626
## Lag 2e+05 -0.01104457
## Lag 3e+05 -0.02498166
## Lag 4e+05 0.01981440
## Lag 5e+05 0.01576917
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 -0.018392805
## Lag 2e+05 -0.022060840
## Lag 3e+05 -0.006122224
## Lag 4e+05 0.002939004
## Lag 5e+05 -0.010027984
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.015469674 0.018826142
## Lag 2e+05 -0.005279943 -0.013621349
## Lag 3e+05 0.002691343 -0.005630995
## Lag 4e+05 -0.005428346 -0.022443196
## Lag 5e+05 -0.004138908 -0.009769093
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.0048462428 0.012539505 0.0046377740
## Lag 2e+05 -0.0001257225 -0.013582644 -0.0210312283
## Lag 3e+05 -0.0167820153 0.002065609 -0.0007765344
## Lag 4e+05 0.0246049394 -0.004653529 0.0057853266
## Lag 5e+05 -0.0041146704 -0.009164514 0.0056567204
## nodematch.race..wa.O
## Lag 0 1.0000000000
## Lag 1e+05 0.0186367963
## Lag 2e+05 -0.0004014425
## Lag 3e+05 0.0022420535
## Lag 4e+05 -0.0116791683
## Lag 5e+05 -0.0386654382
## Chain 3
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.009129557 0.035607342
## Lag 2e+05 0.007910213 -0.014802132
## Lag 3e+05 0.007010217 0.009266843
## Lag 4e+05 -0.022995657 -0.007639349
## Lag 5e+05 -0.010028148 -0.006232587
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.00000000
## Lag 1e+05 0.01835202
## Lag 2e+05 -0.01100983
## Lag 3e+05 0.01307359
## Lag 4e+05 0.00250712
## Lag 5e+05 0.02272802
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.006157808
## Lag 2e+05 0.009006919
## Lag 3e+05 -0.007174803
## Lag 4e+05 -0.016985017
## Lag 5e+05 0.007086260
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.03322234
## Lag 2e+05 -0.01630473
## Lag 3e+05 -0.00562777
## Lag 4e+05 0.01261462
## Lag 5e+05 -0.01525425
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 0.009590951 -0.0117679756
## Lag 2e+05 0.013658217 -0.0178627541
## Lag 3e+05 -0.025477234 -0.0084782023
## Lag 4e+05 -0.013997303 -0.0172666243
## Lag 5e+05 -0.007182599 0.0008559235
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.013657180 0.018611194 -0.03837237
## Lag 2e+05 0.004922150 0.010945167 0.03146729
## Lag 3e+05 -0.022409334 0.006010626 0.01148593
## Lag 4e+05 0.018840674 0.019670644 0.01621037
## Lag 5e+05 -0.002313068 0.014046025 -0.01990896
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 0.032868609
## Lag 2e+05 0.018403751
## Lag 3e+05 0.016451907
## Lag 4e+05 0.004666243
## Lag 5e+05 -0.005575709
## Chain 4
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.001059910 0.016948999
## Lag 2e+05 0.006557898 -0.004513058
## Lag 3e+05 -0.002281785 -0.012719002
## Lag 4e+05 0.003115736 0.031905705
## Lag 5e+05 -0.018541980 -0.031226396
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 -0.021731093
## Lag 2e+05 0.006583539
## Lag 3e+05 -0.018637990
## Lag 4e+05 0.017599383
## Lag 5e+05 0.004565398
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.010532739
## Lag 2e+05 -0.027853442
## Lag 3e+05 0.017901550
## Lag 4e+05 -0.006945901
## Lag 5e+05 0.018588624
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.0000000000
## Lag 1e+05 -0.0005893541
## Lag 2e+05 0.0023775653
## Lag 3e+05 -0.0074189049
## Lag 4e+05 0.0238517378
## Lag 5e+05 -0.0193555029
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.012052125 -0.028701239
## Lag 2e+05 0.015309392 0.004455955
## Lag 3e+05 0.001226807 0.007910515
## Lag 4e+05 -0.004367875 -0.008984838
## Lag 5e+05 0.010980387 -0.022163981
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0047042356 0.016529458 0.035246271
## Lag 2e+05 0.0008282387 -0.032158726 -0.009004717
## Lag 3e+05 0.0212793313 -0.013293962 -0.004259893
## Lag 4e+05 -0.0113257903 -0.033639684 -0.011840840
## Lag 5e+05 0.0009826457 -0.008663648 -0.006249456
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.015137904
## Lag 2e+05 0.006967033
## Lag 3e+05 0.022628362
## Lag 4e+05 0.034160552
## Lag 5e+05 -0.019943541
## Chain 5
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.002681930 0.001219762
## Lag 2e+05 0.004176453 0.001059139
## Lag 3e+05 -0.007743768 -0.002967688
## Lag 4e+05 -0.030749010 0.009512368
## Lag 5e+05 0.008055143 -0.029762941
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.014086793
## Lag 2e+05 -0.005470278
## Lag 3e+05 -0.006043754
## Lag 4e+05 0.009955788
## Lag 5e+05 -0.007788749
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.020168149
## Lag 2e+05 -0.024042963
## Lag 3e+05 0.014847306
## Lag 4e+05 -0.004076071
## Lag 5e+05 0.023915707
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 -0.020370059
## Lag 2e+05 0.003563453
## Lag 3e+05 -0.015229307
## Lag 4e+05 -0.004424009
## Lag 5e+05 0.004512811
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 0.0161162600 0.013253168
## Lag 2e+05 0.0005071354 0.003817778
## Lag 3e+05 -0.0147045524 -0.007922601
## Lag 4e+05 -0.0259076565 -0.032505679
## Lag 5e+05 -0.0020651744 -0.025134211
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 1.000000e+00 1.000000000
## Lag 1e+05 -0.0062994102 4.181164e-05 -0.022698361
## Lag 2e+05 0.0288258077 -8.607390e-03 -0.002034118
## Lag 3e+05 -0.0129872252 -2.464248e-03 0.015758669
## Lag 4e+05 -0.0034746149 -4.674731e-03 -0.011093162
## Lag 5e+05 -0.0004571073 8.239553e-03 0.025753396
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.001589239
## Lag 2e+05 -0.028190999
## Lag 3e+05 -0.001594706
## Lag 4e+05 -0.006910989
## Lag 5e+05 0.002869058
## Chain 6
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.019202128 -0.010518337
## Lag 2e+05 -0.010748543 0.004727266
## Lag 3e+05 0.006726598 -0.019372844
## Lag 4e+05 0.023702703 0.010697968
## Lag 5e+05 0.008666694 0.000717902
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.0000000000
## Lag 1e+05 0.0240741855
## Lag 2e+05 -0.0001780886
## Lag 3e+05 -0.0390591583
## Lag 4e+05 -0.0255068434
## Lag 5e+05 -0.0011909667
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.007601330
## Lag 2e+05 0.015019503
## Lag 3e+05 -0.029712148
## Lag 4e+05 0.008195374
## Lag 5e+05 0.028019248
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.002192101
## Lag 2e+05 -0.003640119
## Lag 3e+05 0.008070529
## Lag 4e+05 -0.020580428
## Lag 5e+05 0.029270326
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000e+00 1.000000000
## Lag 1e+05 1.751613e-03 -0.004516838
## Lag 2e+05 3.340118e-05 0.012130905
## Lag 3e+05 -1.106008e-02 0.013558533
## Lag 4e+05 -6.562737e-03 0.034643902
## Lag 5e+05 1.558904e-02 -0.008444868
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.0000000000
## Lag 1e+05 0.0343995861 0.002498648 -0.0056346453
## Lag 2e+05 0.0011212968 -0.036972817 -0.0300242939
## Lag 3e+05 0.0100553117 -0.007817494 -0.0003767314
## Lag 4e+05 0.0023494822 0.012983063 0.0138080603
## Lag 5e+05 -0.0001803568 0.005759302 0.0011324447
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.006067265
## Lag 2e+05 0.001772246
## Lag 3e+05 0.032528048
## Lag 4e+05 0.039883696
## Lag 5e+05 0.019952030
## Chain 7
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.019176627 -0.016853970
## Lag 2e+05 -0.049892123 -0.007839650
## Lag 3e+05 0.013482975 0.004043043
## Lag 4e+05 -0.001746957 0.011239921
## Lag 5e+05 -0.004556856 0.029070290
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.0000000000
## Lag 1e+05 -0.0166412738
## Lag 2e+05 -0.0009281719
## Lag 3e+05 0.0308636162
## Lag 4e+05 -0.0063423047
## Lag 5e+05 -0.0184842649
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.013050736
## Lag 2e+05 0.012214318
## Lag 3e+05 0.002630459
## Lag 4e+05 -0.004016622
## Lag 5e+05 0.002912891
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.0000000000
## Lag 1e+05 -0.0005597757
## Lag 2e+05 -0.0143705674
## Lag 3e+05 -0.0183778318
## Lag 4e+05 0.0224058168
## Lag 5e+05 0.0100389468
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 -0.007779326 -0.0018968371
## Lag 2e+05 -0.017691231 0.0001606169
## Lag 3e+05 0.026543688 0.0321693101
## Lag 4e+05 0.016814639 -0.0001544169
## Lag 5e+05 0.023014557 0.0009104194
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.0000000000
## Lag 1e+05 0.029971907 -0.03289824 0.0077645067
## Lag 2e+05 -0.014866760 -0.02517302 0.0025388258
## Lag 3e+05 0.016724942 -0.01012301 -0.0120952643
## Lag 4e+05 -0.003488776 -0.02947419 0.0021204182
## Lag 5e+05 0.005022565 -0.01742612 0.0005713957
## nodematch.race..wa.O
## Lag 0 1.0000000000
## Lag 1e+05 -0.0309721842
## Lag 2e+05 -0.0229189443
## Lag 3e+05 0.0005440191
## Lag 4e+05 -0.0149232487
## Lag 5e+05 -0.0276149222
## Chain 8
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 0.0001821106 0.017854471
## Lag 2e+05 0.0003816720 -0.002272151
## Lag 3e+05 -0.0150688647 -0.019199435
## Lag 4e+05 -0.0137277967 -0.011376993
## Lag 5e+05 -0.0112865338 0.003664171
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.007174850
## Lag 2e+05 0.016351147
## Lag 3e+05 0.004760386
## Lag 4e+05 0.009687951
## Lag 5e+05 -0.024348959
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.030494856
## Lag 2e+05 0.004119293
## Lag 3e+05 0.012746356
## Lag 4e+05 -0.020758894
## Lag 5e+05 -0.001992571
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.0000000000
## Lag 1e+05 -0.0076357997
## Lag 2e+05 0.0018111783
## Lag 3e+05 -0.0009793702
## Lag 4e+05 0.0106553987
## Lag 5e+05 0.0062087410
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 0.007565794 0.0055736133
## Lag 2e+05 0.032682004 0.0241057799
## Lag 3e+05 0.006077291 -0.0002379887
## Lag 4e+05 -0.018755530 0.0107091524
## Lag 5e+05 -0.001590387 -0.0033756472
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000e+00
## Lag 1e+05 0.013732138 -0.001949090 -2.289755e-02
## Lag 2e+05 0.013815660 -0.003587892 -5.304174e-04
## Lag 3e+05 -0.007941399 0.010120051 3.491714e-03
## Lag 4e+05 -0.018837060 0.005044419 -7.468697e-03
## Lag 5e+05 -0.006488616 -0.017870868 2.237923e-05
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.001234719
## Lag 2e+05 -0.006421063
## Lag 3e+05 -0.009916610
## Lag 4e+05 -0.025445693
## Lag 5e+05 -0.012808565
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.13191 0.33903
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -1.44395 0.09481
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.25862 0.06446
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.58405 -0.30400
## nodematch.race..wa.B nodematch.race..wa.H
## 0.86978 -1.46359
## nodematch.race..wa.O
## 0.43511
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.8950543 0.7345882
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.1487533 0.9244669
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.7959251 0.9486007
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.5591868 0.7611279
## nodematch.race..wa.B nodematch.race..wa.H
## 0.3844192 0.1433064
## nodematch.race..wa.O
## 0.6634839
## Joint P-value (lower = worse): 0.7817768 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.4203 1.0620
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.1591 -0.4670
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.7608 -1.2450
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.7548 0.2550
## nodematch.race..wa.B nodematch.race..wa.H
## 1.8842 -1.3447
## nodematch.race..wa.O
## -0.5973
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.6742737 0.2882410
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.8735534 0.6405027
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.4467649 0.2131303
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4503556 0.7987616
## nodematch.race..wa.B nodematch.race..wa.H
## 0.0595404 0.1787214
## nodematch.race..wa.O
## 0.5503019
## Joint P-value (lower = worse): 0.1436464 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 1.3962 1.9257
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.1673 -0.2921
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.4929 0.6491
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.4126 -0.4135
## nodematch.race..wa.B nodematch.race..wa.H
## -1.7093 -1.1646
## nodematch.race..wa.O
## 2.0711
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.16265619 0.05413875
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.86710715 0.77019246
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.62209066 0.51626486
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.67988354 0.67924869
## nodematch.race..wa.B nodematch.race..wa.H
## 0.08739300 0.24418030
## nodematch.race..wa.O
## 0.03835179
## Joint P-value (lower = worse): 0.3612008 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.4689 1.0930
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.1442 0.3198
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -1.5961 -0.6879
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.9541 0.1210
## nodematch.race..wa.B nodematch.race..wa.H
## -0.4174 0.1171
## nodematch.race..wa.O
## -0.3837
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.6391537 0.2744022
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.8853123 0.7491385
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.1104628 0.4915313
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.3400228 0.9037162
## nodematch.race..wa.B nodematch.race..wa.H
## 0.6763992 0.9067437
## nodematch.race..wa.O
## 0.7011666
## Joint P-value (lower = worse): 0.8455335 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.3687 -1.0531
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 1.3900 1.1531
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -1.5602 1.3074
## nodefactor.race..wa.B nodefactor.race..wa.H
## -1.0853 -0.3408
## nodematch.race..wa.B nodematch.race..wa.H
## 0.4831 -0.5173
## nodematch.race..wa.O
## 1.0678
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.7123876 0.2922734
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.1645181 0.2488726
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.1187223 0.1910702
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.2778060 0.7332409
## nodematch.race..wa.B nodematch.race..wa.H
## 0.6290573 0.6049567
## nodematch.race..wa.O
## 0.2855977
## Joint P-value (lower = worse): 0.3449352 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.85090 -0.92078
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -1.43987 -1.02754
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -1.66491 -0.38922
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.02584 -2.01699
## nodematch.race..wa.B nodematch.race..wa.H
## 1.74810 -0.62141
## nodematch.race..wa.O
## 0.19689
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.39482256 0.35716417
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.14990417 0.30416538
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.09593007 0.69711230
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.97938614 0.04369614
## nodematch.race..wa.B nodematch.race..wa.H
## 0.08044692 0.53432872
## nodematch.race..wa.O
## 0.84391393
## Joint P-value (lower = worse): 0.4239701 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.1953 0.7327
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.3112 -0.9586
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.9816 -0.4174
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.2383 -0.3234
## nodematch.race..wa.B nodematch.race..wa.H
## -0.1429 -1.6783
## nodematch.race..wa.O
## -0.2484
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.84519306 0.46376388
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.75563243 0.33776872
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.32629231 0.67638907
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.81163310 0.74639660
## nodematch.race..wa.B nodematch.race..wa.H
## 0.88638209 0.09328757
## nodematch.race..wa.O
## 0.80382615
## Joint P-value (lower = worse): 0.8176736 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -1.03716 -1.69863
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.93980 -1.61569
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.03289 -0.40026
## nodefactor.race..wa.B nodefactor.race..wa.H
## -1.42270 -0.55094
## nodematch.race..wa.B nodematch.race..wa.H
## 0.82566 0.40076
## nodematch.race..wa.O
## -0.26801
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.29966048 0.08938892
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.34732006 0.10616215
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.97376046 0.68896709
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.15482325 0.58167301
## nodematch.race..wa.B nodematch.race..wa.H
## 0.40899953 0.68859354
## nodematch.race..wa.O
## 0.78869331
## Joint P-value (lower = worse): 0.6080186 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 5
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 1.62348 22.014 0.127097 0.12838
## nodefactor.deg.main.deg.pers.0.1 0.20089 14.299 0.082557 0.08256
## nodefactor.deg.main.deg.pers.0.2 0.75577 6.193 0.035758 0.03575
## nodefactor.deg.main.deg.pers.1.0 -0.06287 6.311 0.036434 0.03667
## nodefactor.deg.main.deg.pers.1.1 0.84267 12.512 0.072237 0.07284
## nodefactor.deg.main.deg.pers.1.2 0.14948 12.971 0.074890 0.07508
## nodefactor.race..wa.B 0.23525 8.941 0.051623 0.05131
## nodefactor.race..wa.H 0.68355 13.378 0.077236 0.07738
## nodefactor.region.EW 0.26292 9.529 0.055015 0.05504
## nodefactor.region.OW 0.70819 17.530 0.101212 0.10159
## nodematch.race..wa.B 0.07225 1.605 0.009269 0.00921
## nodematch.race..wa.H 0.02098 3.694 0.021325 0.02140
## nodematch.race..wa.O 0.85761 17.028 0.098313 0.09764
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -41.159 -13.159 0.84138 16.841 45.841
## nodefactor.deg.main.deg.pers.0.1 -27.310 -9.310 -0.31004 9.690 28.690
## nodefactor.deg.main.deg.pers.0.2 -10.371 -3.371 0.62897 4.629 13.629
## nodefactor.deg.main.deg.pers.1.0 -12.033 -4.033 -0.03347 3.967 12.967
## nodefactor.deg.main.deg.pers.1.1 -22.538 -7.538 0.46214 9.462 25.462
## nodefactor.deg.main.deg.pers.1.2 -24.388 -8.388 -0.38812 8.612 26.612
## nodefactor.race..wa.B -16.591 -5.591 0.40918 6.409 18.409
## nodefactor.race..wa.H -25.174 -8.174 0.82608 9.826 27.826
## nodefactor.region.EW -17.501 -6.501 0.49862 6.499 19.499
## nodefactor.region.OW -33.486 -11.486 0.51379 12.514 35.514
## nodematch.race..wa.B -2.540 -1.540 -0.53985 1.460 3.460
## nodematch.race..wa.H -6.269 -2.269 -0.26902 2.731 7.731
## nodematch.race..wa.O -31.880 -10.880 1.11998 12.120 34.120
##
##
## Sample statistics cross-correlations:
## edges
## edges 1.00000000
## nodefactor.deg.main.deg.pers.0.1 0.54698097
## nodefactor.deg.main.deg.pers.0.2 0.27373047
## nodefactor.deg.main.deg.pers.1.0 0.27653067
## nodefactor.deg.main.deg.pers.1.1 0.50007230
## nodefactor.deg.main.deg.pers.1.2 0.51732819
## nodefactor.race..wa.B 0.39038739
## nodefactor.race..wa.H 0.51445723
## nodefactor.region.EW 0.40590306
## nodefactor.region.OW 0.63235609
## nodematch.race..wa.B 0.07407811
## nodematch.race..wa.H 0.17390639
## nodematch.race..wa.O 0.77325939
## nodefactor.deg.main.deg.pers.0.1
## edges 0.54698097
## nodefactor.deg.main.deg.pers.0.1 1.00000000
## nodefactor.deg.main.deg.pers.0.2 0.07315837
## nodefactor.deg.main.deg.pers.1.0 0.07656466
## nodefactor.deg.main.deg.pers.1.1 0.13386176
## nodefactor.deg.main.deg.pers.1.2 0.14276917
## nodefactor.race..wa.B 0.24349541
## nodefactor.race..wa.H 0.24701797
## nodefactor.region.EW 0.20641904
## nodefactor.region.OW 0.34857161
## nodematch.race..wa.B 0.05706259
## nodematch.race..wa.H 0.07065458
## nodematch.race..wa.O 0.43167346
## nodefactor.deg.main.deg.pers.0.2
## edges 0.27373047
## nodefactor.deg.main.deg.pers.0.1 0.07315837
## nodefactor.deg.main.deg.pers.0.2 1.00000000
## nodefactor.deg.main.deg.pers.1.0 0.03535950
## nodefactor.deg.main.deg.pers.1.1 0.06461245
## nodefactor.deg.main.deg.pers.1.2 0.07731024
## nodefactor.race..wa.B 0.12005774
## nodefactor.race..wa.H 0.13659726
## nodefactor.region.EW 0.10475302
## nodefactor.region.OW 0.18310732
## nodematch.race..wa.B 0.03313109
## nodematch.race..wa.H 0.03717945
## nodematch.race..wa.O 0.20698197
## nodefactor.deg.main.deg.pers.1.0
## edges 0.276530672
## nodefactor.deg.main.deg.pers.0.1 0.076564660
## nodefactor.deg.main.deg.pers.0.2 0.035359503
## nodefactor.deg.main.deg.pers.1.0 1.000000000
## nodefactor.deg.main.deg.pers.1.1 0.070743960
## nodefactor.deg.main.deg.pers.1.2 0.071381852
## nodefactor.race..wa.B 0.098729992
## nodefactor.race..wa.H 0.155421516
## nodefactor.region.EW 0.117599309
## nodefactor.region.OW 0.175263120
## nodematch.race..wa.B 0.008465332
## nodematch.race..wa.H 0.054725791
## nodematch.race..wa.O 0.208391555
## nodefactor.deg.main.deg.pers.1.1
## edges 0.50007230
## nodefactor.deg.main.deg.pers.0.1 0.13386176
## nodefactor.deg.main.deg.pers.0.2 0.06461245
## nodefactor.deg.main.deg.pers.1.0 0.07074396
## nodefactor.deg.main.deg.pers.1.1 1.00000000
## nodefactor.deg.main.deg.pers.1.2 0.13239612
## nodefactor.race..wa.B 0.16660200
## nodefactor.race..wa.H 0.30745794
## nodefactor.region.EW 0.21202121
## nodefactor.region.OW 0.32042640
## nodematch.race..wa.B 0.01899502
## nodematch.race..wa.H 0.12267369
## nodematch.race..wa.O 0.37080725
## nodefactor.deg.main.deg.pers.1.2
## edges 0.51732819
## nodefactor.deg.main.deg.pers.0.1 0.14276917
## nodefactor.deg.main.deg.pers.0.2 0.07731024
## nodefactor.deg.main.deg.pers.1.0 0.07138185
## nodefactor.deg.main.deg.pers.1.1 0.13239612
## nodefactor.deg.main.deg.pers.1.2 1.00000000
## nodefactor.race..wa.B 0.16689152
## nodefactor.race..wa.H 0.31698354
## nodefactor.region.EW 0.21581508
## nodefactor.region.OW 0.31412841
## nodematch.race..wa.B 0.03027283
## nodematch.race..wa.H 0.12310470
## nodematch.race..wa.O 0.38501227
## nodefactor.race..wa.B
## edges 0.390387395
## nodefactor.deg.main.deg.pers.0.1 0.243495410
## nodefactor.deg.main.deg.pers.0.2 0.120057737
## nodefactor.deg.main.deg.pers.1.0 0.098729992
## nodefactor.deg.main.deg.pers.1.1 0.166602002
## nodefactor.deg.main.deg.pers.1.2 0.166891522
## nodefactor.race..wa.B 1.000000000
## nodefactor.race..wa.H 0.149520973
## nodefactor.region.EW 0.108743875
## nodefactor.region.OW 0.217949245
## nodematch.race..wa.B 0.347929726
## nodematch.race..wa.H 0.010583217
## nodematch.race..wa.O 0.004615852
## nodefactor.race..wa.H
## edges 0.514457226
## nodefactor.deg.main.deg.pers.0.1 0.247017968
## nodefactor.deg.main.deg.pers.0.2 0.136597261
## nodefactor.deg.main.deg.pers.1.0 0.155421516
## nodefactor.deg.main.deg.pers.1.1 0.307457943
## nodefactor.deg.main.deg.pers.1.2 0.316983543
## nodefactor.race..wa.B 0.149520973
## nodefactor.race..wa.H 1.000000000
## nodefactor.region.EW 0.316311241
## nodefactor.region.OW 0.297628014
## nodematch.race..wa.B -0.008599605
## nodematch.race..wa.H 0.557221297
## nodematch.race..wa.O -0.003301540
## nodefactor.region.EW nodefactor.region.OW
## edges 0.405903058 0.63235609
## nodefactor.deg.main.deg.pers.0.1 0.206419044 0.34857161
## nodefactor.deg.main.deg.pers.0.2 0.104753018 0.18310732
## nodefactor.deg.main.deg.pers.1.0 0.117599309 0.17526312
## nodefactor.deg.main.deg.pers.1.1 0.212021213 0.32042640
## nodefactor.deg.main.deg.pers.1.2 0.215815084 0.31412841
## nodefactor.race..wa.B 0.108743875 0.21794925
## nodefactor.race..wa.H 0.316311241 0.29762801
## nodefactor.region.EW 1.000000000 0.12657911
## nodefactor.region.OW 0.126579115 1.00000000
## nodematch.race..wa.B 0.006234486 0.03143011
## nodematch.race..wa.H 0.144255738 0.09257487
## nodematch.race..wa.O 0.272290280 0.51537745
## nodematch.race..wa.B nodematch.race..wa.H
## edges 0.074078110 0.173906393
## nodefactor.deg.main.deg.pers.0.1 0.057062592 0.070654584
## nodefactor.deg.main.deg.pers.0.2 0.033131088 0.037179445
## nodefactor.deg.main.deg.pers.1.0 0.008465332 0.054725791
## nodefactor.deg.main.deg.pers.1.1 0.018995021 0.122673693
## nodefactor.deg.main.deg.pers.1.2 0.030272830 0.123104699
## nodefactor.race..wa.B 0.347929726 0.010583217
## nodefactor.race..wa.H -0.008599605 0.557221297
## nodefactor.region.EW 0.006234486 0.144255738
## nodefactor.region.OW 0.031430111 0.092574873
## nodematch.race..wa.B 1.000000000 -0.004431205
## nodematch.race..wa.H -0.004431205 1.000000000
## nodematch.race..wa.O 0.009287558 -0.001295523
## nodematch.race..wa.O
## edges 0.773259394
## nodefactor.deg.main.deg.pers.0.1 0.431673455
## nodefactor.deg.main.deg.pers.0.2 0.206981974
## nodefactor.deg.main.deg.pers.1.0 0.208391555
## nodefactor.deg.main.deg.pers.1.1 0.370807248
## nodefactor.deg.main.deg.pers.1.2 0.385012267
## nodefactor.race..wa.B 0.004615852
## nodefactor.race..wa.H -0.003301540
## nodefactor.region.EW 0.272290280
## nodefactor.region.OW 0.515377450
## nodematch.race..wa.B 0.009287558
## nodematch.race..wa.H -0.001295523
## nodematch.race..wa.O 1.000000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000e+00
## Lag 1e+05 0.016187796 -6.080791e-06
## Lag 2e+05 0.035889104 2.539743e-02
## Lag 3e+05 -0.004815608 2.638197e-03
## Lag 4e+05 0.013940186 1.047890e-02
## Lag 5e+05 0.014555359 -1.047504e-02
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 -0.026983914
## Lag 2e+05 0.031497027
## Lag 3e+05 -0.001314500
## Lag 4e+05 0.005395386
## Lag 5e+05 -0.001492040
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.020309811
## Lag 2e+05 0.014430842
## Lag 3e+05 0.007767718
## Lag 4e+05 0.013677005
## Lag 5e+05 -0.013002634
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.039411835
## Lag 2e+05 0.049264453
## Lag 3e+05 -0.002273479
## Lag 4e+05 -0.016241724
## Lag 5e+05 -0.011245850
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 0.002897701 0.0158022256
## Lag 2e+05 0.042136143 -0.0187874230
## Lag 3e+05 -0.025349143 0.0046895835
## Lag 4e+05 -0.010519368 -0.0214814123
## Lag 5e+05 -0.002834213 -0.0004351698
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.0000000000
## Lag 1e+05 0.016197258 0.01571075 -0.0149817464
## Lag 2e+05 0.021309854 0.03553240 0.0224115463
## Lag 3e+05 -0.004592280 0.01098566 -0.0007759948
## Lag 4e+05 0.002669643 -0.02401224 0.0046237857
## Lag 5e+05 0.022020881 -0.00674200 0.0070924428
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.011085245 0.01302072 0.018937889
## Lag 2e+05 0.001170427 -0.00485414 0.017129349
## Lag 3e+05 0.016559854 -0.03524559 0.004044786
## Lag 4e+05 -0.009678258 -0.01029398 0.021489725
## Lag 5e+05 -0.022496170 0.02747241 0.007135001
## Chain 2
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.025808881 0.014874471
## Lag 2e+05 -0.018672989 -0.006319465
## Lag 3e+05 -0.012627632 0.003781696
## Lag 4e+05 -0.007924405 0.022640347
## Lag 5e+05 -0.002355406 0.001152860
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 -0.004484704
## Lag 2e+05 0.005705293
## Lag 3e+05 0.005694072
## Lag 4e+05 0.005859980
## Lag 5e+05 0.019077508
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.029921933
## Lag 2e+05 -0.017784703
## Lag 3e+05 0.013780156
## Lag 4e+05 0.008561375
## Lag 5e+05 0.026534941
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.004760250
## Lag 2e+05 -0.017690671
## Lag 3e+05 -0.001872405
## Lag 4e+05 0.031607497
## Lag 5e+05 -0.021708320
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.015241024 -0.010468802
## Lag 2e+05 -0.014795533 -0.002238248
## Lag 3e+05 -0.013132322 0.004991271
## Lag 4e+05 -0.002340100 0.013534852
## Lag 5e+05 -0.003920717 0.015296337
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.014625937 0.018301845 -0.005909434
## Lag 2e+05 0.025614759 -0.001089658 -0.011271204
## Lag 3e+05 0.033889540 -0.012472880 -0.007964291
## Lag 4e+05 -0.005359342 -0.005070644 0.006041083
## Lag 5e+05 -0.013250215 0.004103074 -0.011576843
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.000450558 -0.019260723 -0.007114380
## Lag 2e+05 0.022726222 -0.004509393 -0.015157074
## Lag 3e+05 0.006580707 0.024635554 -0.036194078
## Lag 4e+05 0.016328177 -0.002175387 0.016501711
## Lag 5e+05 0.028591996 -0.003185432 0.004673038
## Chain 3
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 0.0084915532 0.004804579
## Lag 2e+05 0.0005003996 0.020992385
## Lag 3e+05 -0.0024895965 0.006386404
## Lag 4e+05 0.0127201769 0.006978649
## Lag 5e+05 -0.0275298284 -0.028576743
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 -0.004500237
## Lag 2e+05 -0.032045358
## Lag 3e+05 -0.009579918
## Lag 4e+05 0.004480320
## Lag 5e+05 0.017903418
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.00000000
## Lag 1e+05 -0.01302048
## Lag 2e+05 0.04291323
## Lag 3e+05 0.01291883
## Lag 4e+05 0.01307447
## Lag 5e+05 0.01050905
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 -0.006295635
## Lag 2e+05 0.016519343
## Lag 3e+05 -0.012641472
## Lag 4e+05 -0.015811503
## Lag 5e+05 0.003499972
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.003577572 -0.016691834
## Lag 2e+05 0.001255261 -0.023192229
## Lag 3e+05 0.002768212 -0.010670448
## Lag 4e+05 -0.020039010 0.006324612
## Lag 5e+05 0.012284444 -0.030427221
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.004538907 0.013618688 -0.016210929
## Lag 2e+05 0.013299149 -0.003682552 -0.003118138
## Lag 3e+05 0.021698927 0.010947242 0.001433354
## Lag 4e+05 -0.017846619 0.003011765 0.015467071
## Lag 5e+05 0.024550751 -0.024080057 -0.005003325
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.014250337 -0.002223484 0.001468003
## Lag 2e+05 0.005338555 0.032950624 0.015375824
## Lag 3e+05 -0.010285393 -0.004282717 -0.018018621
## Lag 4e+05 -0.017786173 -0.009882906 0.027185809
## Lag 5e+05 0.034405241 0.004638316 -0.011803023
## Chain 4
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.007181045 -0.008926829
## Lag 2e+05 0.006165675 -0.002933789
## Lag 3e+05 -0.023244701 -0.015697014
## Lag 4e+05 -0.020476668 0.011638486
## Lag 5e+05 0.044900217 0.006601123
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.013787287
## Lag 2e+05 -0.001509463
## Lag 3e+05 0.005680327
## Lag 4e+05 0.008475170
## Lag 5e+05 -0.009995165
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.011150762
## Lag 2e+05 0.022564669
## Lag 3e+05 -0.026838713
## Lag 4e+05 0.009369764
## Lag 5e+05 -0.013928661
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.022827577
## Lag 2e+05 0.020383892
## Lag 3e+05 0.009649934
## Lag 4e+05 -0.015160127
## Lag 5e+05 0.009460995
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.008516214 -0.015273279
## Lag 2e+05 -0.010654300 0.025182987
## Lag 3e+05 0.017249631 -0.001743646
## Lag 4e+05 -0.010454873 -0.002231422
## Lag 5e+05 0.001629581 -0.006950509
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.004891430 0.012167807 -0.008940523
## Lag 2e+05 0.018197499 0.007859965 0.001569574
## Lag 3e+05 -0.007156353 -0.023455037 0.025253604
## Lag 4e+05 0.002475056 0.007218500 0.004705354
## Lag 5e+05 0.023224531 0.015789052 0.008739745
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.012558346 0.003380345 -0.005463362
## Lag 2e+05 0.016821101 -0.028793083 0.018928766
## Lag 3e+05 -0.018032099 -0.014982218 -0.011786398
## Lag 4e+05 -0.004597407 0.001619151 -0.023827183
## Lag 5e+05 -0.011499764 -0.038888554 0.032097054
## Chain 5
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.017726119 0.009010180
## Lag 2e+05 -0.003951497 0.005962325
## Lag 3e+05 -0.026814638 -0.007085802
## Lag 4e+05 0.009079379 -0.012094440
## Lag 5e+05 0.031726000 0.017177614
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.009342523
## Lag 2e+05 0.003125512
## Lag 3e+05 -0.051444390
## Lag 4e+05 -0.002926697
## Lag 5e+05 0.014531902
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.0000000000
## Lag 1e+05 0.0291205014
## Lag 2e+05 0.0057613613
## Lag 3e+05 0.0027061709
## Lag 4e+05 -0.0007577756
## Lag 5e+05 0.0288895120
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.010596463
## Lag 2e+05 -0.008478730
## Lag 3e+05 0.000727426
## Lag 4e+05 0.008629513
## Lag 5e+05 0.016019013
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.016521341 0.015575512
## Lag 2e+05 0.009592362 -0.040774724
## Lag 3e+05 -0.012550423 -0.001669356
## Lag 4e+05 0.024920110 -0.001465227
## Lag 5e+05 0.009610598 0.033741177
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.020440290 -0.012961884 0.028763353
## Lag 2e+05 -0.009118616 -0.036623073 0.008335284
## Lag 3e+05 -0.004680362 -0.018087364 -0.003484183
## Lag 4e+05 -0.012142987 0.027257672 -0.001067927
## Lag 5e+05 0.036799402 -0.008348502 0.004455817
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.0000000000 1.000000000 1.0000000000
## Lag 1e+05 0.0106207614 -0.029037956 0.0092173230
## Lag 2e+05 -0.0287581360 -0.013123757 0.0059646293
## Lag 3e+05 0.0065841654 -0.001467524 -0.0141496019
## Lag 4e+05 -0.0139472489 -0.011468966 0.0254439753
## Lag 5e+05 -0.0001976772 0.009503273 0.0008242743
## Chain 6
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.005971222 -0.011789512
## Lag 2e+05 -0.006798380 0.008531680
## Lag 3e+05 -0.017116356 -0.005021223
## Lag 4e+05 0.017698729 0.034507615
## Lag 5e+05 -0.021020044 -0.008990807
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 -0.013668835
## Lag 2e+05 0.009342533
## Lag 3e+05 -0.005661909
## Lag 4e+05 -0.000184554
## Lag 5e+05 0.002787699
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000e+00
## Lag 1e+05 -1.328340e-02
## Lag 2e+05 -2.977274e-02
## Lag 3e+05 1.113210e-02
## Lag 4e+05 -2.346306e-02
## Lag 5e+05 -8.003673e-05
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 -0.028531931
## Lag 2e+05 -0.005548763
## Lag 3e+05 0.006734491
## Lag 4e+05 0.010003939
## Lag 5e+05 0.001179411
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.020599813 -0.019342355
## Lag 2e+05 0.018666733 -0.009382535
## Lag 3e+05 -0.003771718 0.006182426
## Lag 4e+05 0.007482887 -0.020704786
## Lag 5e+05 -0.007049267 0.014651864
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000e+00
## Lag 1e+05 -0.000758072 -0.012610918 -1.218094e-02
## Lag 2e+05 0.029472570 0.005971863 1.100718e-02
## Lag 3e+05 0.007896493 0.025167791 -1.130805e-02
## Lag 4e+05 0.012749742 -0.019024108 -2.147598e-02
## Lag 5e+05 -0.001601906 -0.008231985 2.160392e-05
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.027067061 -0.002249415 -0.0013603693
## Lag 2e+05 0.004383052 -0.006356829 0.0006718527
## Lag 3e+05 0.006736349 0.029853690 0.0053508649
## Lag 4e+05 -0.012806912 -0.005222702 0.0171963308
## Lag 5e+05 -0.014591903 -0.031030545 -0.0036755822
## Chain 7
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.003553832 -0.010549727
## Lag 2e+05 0.011659077 0.023768406
## Lag 3e+05 0.002234245 0.004044059
## Lag 4e+05 -0.008151224 0.015959145
## Lag 5e+05 -0.010113679 0.004840702
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.028663873
## Lag 2e+05 0.002122945
## Lag 3e+05 -0.004857900
## Lag 4e+05 -0.003089562
## Lag 5e+05 0.002389556
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.007977949
## Lag 2e+05 0.006041645
## Lag 3e+05 0.012961738
## Lag 4e+05 -0.001734431
## Lag 5e+05 -0.035007421
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 -0.003826189
## Lag 2e+05 0.003114630
## Lag 3e+05 0.012073550
## Lag 4e+05 -0.017093805
## Lag 5e+05 -0.035589715
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.011168927 -0.009282821
## Lag 2e+05 -0.020159471 0.011231036
## Lag 3e+05 -0.012426522 0.008898788
## Lag 4e+05 -0.009987287 -0.011359528
## Lag 5e+05 -0.017810783 0.008590669
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.010408825 -0.008296128 -0.013465459
## Lag 2e+05 0.017235491 0.021405273 -0.011372905
## Lag 3e+05 -0.004019387 0.008443309 -0.003311734
## Lag 4e+05 0.025667656 0.003171449 -0.001280544
## Lag 5e+05 -0.013764937 0.013931209 -0.006109528
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.025554548 -0.024545388 -0.011615100
## Lag 2e+05 0.007795234 0.019322198 0.011500635
## Lag 3e+05 -0.004262554 0.013800374 0.010936985
## Lag 4e+05 -0.022779731 0.008729107 -0.008638700
## Lag 5e+05 0.007446361 0.011046017 -0.008250481
## Chain 8
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.005069383 -0.018044988
## Lag 2e+05 -0.010334212 -0.010275091
## Lag 3e+05 -0.013650254 0.016700492
## Lag 4e+05 -0.009865934 -0.007406708
## Lag 5e+05 -0.005268607 -0.007708503
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.0000000000
## Lag 1e+05 0.0168127237
## Lag 2e+05 0.0175897498
## Lag 3e+05 -0.0004593286
## Lag 4e+05 -0.0065832132
## Lag 5e+05 0.0110405937
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.0000000000
## Lag 1e+05 -0.0002281938
## Lag 2e+05 -0.0048206282
## Lag 3e+05 0.0156149835
## Lag 4e+05 -0.0269303930
## Lag 5e+05 0.0008078189
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.001805101
## Lag 2e+05 -0.003778046
## Lag 3e+05 -0.034483184
## Lag 4e+05 -0.003721362
## Lag 5e+05 -0.007868365
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 -0.003169739 -0.0272518996
## Lag 2e+05 -0.016412202 -0.0110820139
## Lag 3e+05 -0.010433134 -0.0286281622
## Lag 4e+05 -0.007904731 0.0006034726
## Lag 5e+05 0.031314238 0.0029330850
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.011217684 -0.005225322 0.017203804
## Lag 2e+05 -0.032853827 -0.005382127 0.005929183
## Lag 3e+05 -0.025722727 0.002433176 0.015754378
## Lag 4e+05 0.011777422 0.009590229 -0.004057207
## Lag 5e+05 -0.003653772 -0.008425894 -0.014015313
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.011570387 0.044408355 0.0119196963
## Lag 2e+05 0.008632663 -0.018391653 0.0004945316
## Lag 3e+05 -0.010860957 -0.004435905 0.0010524887
## Lag 4e+05 0.013476416 -0.001845879 -0.0190603888
## Lag 5e+05 -0.010181414 0.032614440 -0.0126100411
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 1.7843 1.8917
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 1.3817 -1.9596
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 1.5287 0.4920
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.9590 1.0851
## nodefactor.region.EW nodefactor.region.OW
## 0.9089 0.6196
## nodematch.race..wa.B nodematch.race..wa.H
## 0.5823 1.6073
## nodematch.race..wa.O
## 1.3150
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.07437570 0.05853062
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.16705941 0.05004117
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.12633488 0.62269077
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.33755750 0.27788916
## nodefactor.region.EW nodefactor.region.OW
## 0.36342741 0.53551275
## nodematch.race..wa.B nodematch.race..wa.H
## 0.56039468 0.10799513
## nodematch.race..wa.O
## 0.18851157
## Joint P-value (lower = worse): 0.504595 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.7010 0.7674
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.1773 -1.3263
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.8840 -0.5336
## nodefactor.race..wa.B nodefactor.race..wa.H
## -1.3129 -0.6693
## nodefactor.region.EW nodefactor.region.OW
## 0.1500 -0.5449
## nodematch.race..wa.B nodematch.race..wa.H
## -1.4746 -0.1918
## nodematch.race..wa.O
## 0.1399
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.4832834 0.4428611
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.8592938 0.1847543
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.3766750 0.5936415
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.1892326 0.5033230
## nodefactor.region.EW nodefactor.region.OW
## 0.8807348 0.5858193
## nodematch.race..wa.B nodematch.race..wa.H
## 0.1403098 0.8479130
## nodematch.race..wa.O
## 0.8887477
## Joint P-value (lower = worse): 0.8511137 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -1.29639 -2.22675
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.94649 -1.99026
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.08967 -1.05262
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.20753 -1.77403
## nodefactor.region.EW nodefactor.region.OW
## -0.35745 -0.46134
## nodematch.race..wa.B nodematch.race..wa.H
## 0.65925 -0.62521
## nodematch.race..wa.O
## -0.77535
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.19484164 0.02596429
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.34389654 0.04656223
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.92854854 0.29251544
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.83559351 0.07605791
## nodefactor.region.EW nodefactor.region.OW
## 0.72075315 0.64455820
## nodematch.race..wa.B nodematch.race..wa.H
## 0.50973536 0.53183057
## nodematch.race..wa.O
## 0.43813220
## Joint P-value (lower = worse): 0.6429223 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.6883 0.0512
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.9041 1.1217
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.9404 0.2051
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.1345 -1.0932
## nodefactor.region.EW nodefactor.region.OW
## 0.4168 -0.6631
## nodematch.race..wa.B nodematch.race..wa.H
## -0.6502 -0.5754
## nodematch.race..wa.O
## 1.6280
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.4912639 0.9591683
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.3659210 0.2619718
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.3470367 0.8374691
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.8930048 0.2743016
## nodefactor.region.EW nodefactor.region.OW
## 0.6768181 0.5072628
## nodematch.race..wa.B nodematch.race..wa.H
## 0.5155822 0.5650431
## nodematch.race..wa.O
## 0.1035199
## Joint P-value (lower = worse): 0.5253492 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.71618 -0.96895
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.93651 -0.32185
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 1.83682 -0.04303
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.31781 1.09640
## nodefactor.region.EW nodefactor.region.OW
## -0.88499 0.63616
## nodematch.race..wa.B nodematch.race..wa.H
## 0.75294 1.84861
## nodematch.race..wa.O
## -1.05642
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.47387936 0.33256927
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.34900844 0.74756330
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.06623691 0.96567590
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.75063082 0.27290519
## nodefactor.region.EW nodefactor.region.OW
## 0.37616204 0.52467517
## nodematch.race..wa.B nodematch.race..wa.H
## 0.45148823 0.06451471
## nodematch.race..wa.O
## 0.29077764
## Joint P-value (lower = worse): 0.677236 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.899187 1.143623
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.318935 -0.541440
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 1.378919 1.299638
## nodefactor.race..wa.B nodefactor.race..wa.H
## -2.452942 -0.007241
## nodefactor.region.EW nodefactor.region.OW
## 0.577695 0.832607
## nodematch.race..wa.B nodematch.race..wa.H
## -0.854085 -0.579388
## nodematch.race..wa.O
## 1.978759
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.36855327 0.25277987
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.74977557 0.58820456
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.16791963 0.19372497
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.01416933 0.99422249
## nodefactor.region.EW nodefactor.region.OW
## 0.56347024 0.40506622
## nodematch.race..wa.B nodematch.race..wa.H
## 0.39305806 0.56232771
## nodematch.race..wa.O
## 0.04784311
## Joint P-value (lower = worse): 0.3866231 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -1.18529 -0.82258
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -1.97523 -0.25778
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.21842 0.03591
## nodefactor.race..wa.B nodefactor.race..wa.H
## -2.84105 0.15812
## nodefactor.region.EW nodefactor.region.OW
## -0.67436 -1.28251
## nodematch.race..wa.B nodematch.race..wa.H
## 1.24653 1.61562
## nodematch.race..wa.O
## -0.17450
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.235902204 0.410748106
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.048241718 0.796577585
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.827102294 0.971355500
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.004496516 0.874358872
## nodefactor.region.EW nodefactor.region.OW
## 0.500084455 0.199663178
## nodematch.race..wa.B nodematch.race..wa.H
## 0.212568348 0.106175711
## nodematch.race..wa.O
## 0.861471922
## Joint P-value (lower = worse): 0.2190713 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 1.0317 0.5784
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 1.3558 1.1666
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.1895 0.8031
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.4648 -0.2177
## nodefactor.region.EW nodefactor.region.OW
## 0.9695 -1.0944
## nodematch.race..wa.B nodematch.race..wa.H
## 0.8819 -0.4331
## nodematch.race..wa.O
## 1.7517
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.30220597 0.56301648
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.17517569 0.24339191
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.84972531 0.42189191
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.64210965 0.82769544
## nodefactor.region.EW nodefactor.region.OW
## 0.33231490 0.27378439
## nodematch.race..wa.B nodematch.race..wa.H
## 0.37782064 0.66490695
## nodematch.race..wa.O
## 0.07982862
## Joint P-value (lower = worse): 0.7217915 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 6
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 1.189713 21.924 0.126581 0.127841
## nodefactor.deg.main.deg.pers.0.1 0.645693 14.437 0.083355 0.083802
## nodefactor.deg.main.deg.pers.0.2 -0.041767 6.139 0.035446 0.035761
## nodefactor.deg.main.deg.pers.1.0 0.270196 6.334 0.036571 0.036368
## nodefactor.deg.main.deg.pers.1.1 0.489669 12.447 0.071863 0.072389
## nodefactor.deg.main.deg.pers.1.2 -0.210424 12.914 0.074558 0.075448
## nodefactor.race..wa.B 0.175551 8.981 0.051852 0.051847
## nodefactor.race..wa.H 0.332147 13.145 0.075895 0.075898
## nodefactor.region.EW 0.035258 9.540 0.055078 0.055250
## nodefactor.region.OW 0.647261 17.470 0.100861 0.100780
## nodematch.race..wa.B 0.009549 1.604 0.009259 0.009178
## nodematch.race..wa.H -0.037154 3.636 0.020992 0.021066
## nodematch.race..wa.O 0.688310 17.051 0.098446 0.099696
## absdiff.sqrt.age 0.998502 22.767 0.131446 0.129407
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -41.159 -13.159 0.84138 15.841 44.841
## nodefactor.deg.main.deg.pers.0.1 -27.310 -9.310 0.68996 10.690 29.690
## nodefactor.deg.main.deg.pers.0.2 -11.371 -4.371 -0.37103 3.629 12.629
## nodefactor.deg.main.deg.pers.1.0 -12.033 -4.033 -0.03347 3.967 12.967
## nodefactor.deg.main.deg.pers.1.1 -23.538 -7.538 0.46214 8.462 25.462
## nodefactor.deg.main.deg.pers.1.2 -25.388 -9.388 -0.38812 8.612 25.612
## nodefactor.race..wa.B -16.591 -5.591 0.40918 6.409 18.409
## nodefactor.race..wa.H -25.174 -9.174 -0.17392 8.826 26.826
## nodefactor.region.EW -18.501 -6.501 -0.50138 6.499 19.499
## nodefactor.region.OW -33.486 -11.486 0.51379 12.514 35.514
## nodematch.race..wa.B -2.540 -1.540 -0.53985 1.460 3.460
## nodematch.race..wa.H -6.269 -2.269 -0.26902 2.731 7.731
## nodematch.race..wa.O -31.880 -10.880 0.11998 12.120 34.120
## absdiff.sqrt.age -42.491 -14.528 0.56312 16.160 46.556
##
##
## Sample statistics cross-correlations:
## edges
## edges 1.00000000
## nodefactor.deg.main.deg.pers.0.1 0.55591212
## nodefactor.deg.main.deg.pers.0.2 0.26443810
## nodefactor.deg.main.deg.pers.1.0 0.27345813
## nodefactor.deg.main.deg.pers.1.1 0.49603061
## nodefactor.deg.main.deg.pers.1.2 0.50830955
## nodefactor.race..wa.B 0.38338069
## nodefactor.race..wa.H 0.50800263
## nodefactor.region.EW 0.39562963
## nodefactor.region.OW 0.63415240
## nodematch.race..wa.B 0.07499615
## nodematch.race..wa.H 0.16035207
## nodematch.race..wa.O 0.77849422
## absdiff.sqrt.age 0.76458382
## nodefactor.deg.main.deg.pers.0.1
## edges 0.55591212
## nodefactor.deg.main.deg.pers.0.1 1.00000000
## nodefactor.deg.main.deg.pers.0.2 0.06458471
## nodefactor.deg.main.deg.pers.1.0 0.07574228
## nodefactor.deg.main.deg.pers.1.1 0.14151839
## nodefactor.deg.main.deg.pers.1.2 0.13519402
## nodefactor.race..wa.B 0.23244591
## nodefactor.race..wa.H 0.24274724
## nodefactor.region.EW 0.21062606
## nodefactor.region.OW 0.35938522
## nodematch.race..wa.B 0.05613736
## nodematch.race..wa.H 0.05916797
## nodematch.race..wa.O 0.44725962
## absdiff.sqrt.age 0.42067332
## nodefactor.deg.main.deg.pers.0.2
## edges 0.26443810
## nodefactor.deg.main.deg.pers.0.1 0.06458471
## nodefactor.deg.main.deg.pers.0.2 1.00000000
## nodefactor.deg.main.deg.pers.1.0 0.02791415
## nodefactor.deg.main.deg.pers.1.1 0.06020182
## nodefactor.deg.main.deg.pers.1.2 0.07021680
## nodefactor.race..wa.B 0.12182483
## nodefactor.race..wa.H 0.12275809
## nodefactor.region.EW 0.08936127
## nodefactor.region.OW 0.18049539
## nodematch.race..wa.B 0.03602835
## nodematch.race..wa.H 0.04155517
## nodematch.race..wa.O 0.20627708
## absdiff.sqrt.age 0.20463286
## nodefactor.deg.main.deg.pers.1.0
## edges 0.27345813
## nodefactor.deg.main.deg.pers.0.1 0.07574228
## nodefactor.deg.main.deg.pers.0.2 0.02791415
## nodefactor.deg.main.deg.pers.1.0 1.00000000
## nodefactor.deg.main.deg.pers.1.1 0.05876234
## nodefactor.deg.main.deg.pers.1.2 0.06787868
## nodefactor.race..wa.B 0.10402766
## nodefactor.race..wa.H 0.15012639
## nodefactor.region.EW 0.11598603
## nodefactor.region.OW 0.17433451
## nodematch.race..wa.B 0.01862807
## nodematch.race..wa.H 0.05601726
## nodematch.race..wa.O 0.20757918
## absdiff.sqrt.age 0.20972047
## nodefactor.deg.main.deg.pers.1.1
## edges 0.49603061
## nodefactor.deg.main.deg.pers.0.1 0.14151839
## nodefactor.deg.main.deg.pers.0.2 0.06020182
## nodefactor.deg.main.deg.pers.1.0 0.05876234
## nodefactor.deg.main.deg.pers.1.1 1.00000000
## nodefactor.deg.main.deg.pers.1.2 0.12448677
## nodefactor.race..wa.B 0.15804092
## nodefactor.race..wa.H 0.30050851
## nodefactor.region.EW 0.20430064
## nodefactor.region.OW 0.31338347
## nodematch.race..wa.B 0.01985172
## nodematch.race..wa.H 0.10665281
## nodematch.race..wa.O 0.37033710
## absdiff.sqrt.age 0.37142781
## nodefactor.deg.main.deg.pers.1.2
## edges 0.50830955
## nodefactor.deg.main.deg.pers.0.1 0.13519402
## nodefactor.deg.main.deg.pers.0.2 0.07021680
## nodefactor.deg.main.deg.pers.1.0 0.06787868
## nodefactor.deg.main.deg.pers.1.1 0.12448677
## nodefactor.deg.main.deg.pers.1.2 1.00000000
## nodefactor.race..wa.B 0.15912636
## nodefactor.race..wa.H 0.30731481
## nodefactor.region.EW 0.20898824
## nodefactor.region.OW 0.31426151
## nodematch.race..wa.B 0.01904716
## nodematch.race..wa.H 0.10949766
## nodematch.race..wa.O 0.37971808
## absdiff.sqrt.age 0.39259709
## nodefactor.race..wa.B
## edges 0.383380695
## nodefactor.deg.main.deg.pers.0.1 0.232445908
## nodefactor.deg.main.deg.pers.0.2 0.121824835
## nodefactor.deg.main.deg.pers.1.0 0.104027664
## nodefactor.deg.main.deg.pers.1.1 0.158040916
## nodefactor.deg.main.deg.pers.1.2 0.159126360
## nodefactor.race..wa.B 1.000000000
## nodefactor.race..wa.H 0.140438360
## nodefactor.region.EW 0.112324052
## nodefactor.region.OW 0.211380859
## nodematch.race..wa.B 0.357616998
## nodematch.race..wa.H 0.000698402
## nodematch.race..wa.O 0.001434573
## absdiff.sqrt.age 0.303108337
## nodefactor.race..wa.H
## edges 5.080026e-01
## nodefactor.deg.main.deg.pers.0.1 2.427472e-01
## nodefactor.deg.main.deg.pers.0.2 1.227581e-01
## nodefactor.deg.main.deg.pers.1.0 1.501264e-01
## nodefactor.deg.main.deg.pers.1.1 3.005085e-01
## nodefactor.deg.main.deg.pers.1.2 3.073148e-01
## nodefactor.race..wa.B 1.404384e-01
## nodefactor.race..wa.H 1.000000e+00
## nodefactor.region.EW 3.028779e-01
## nodefactor.region.OW 2.977940e-01
## nodematch.race..wa.B 2.479265e-03
## nodematch.race..wa.H 5.466943e-01
## nodematch.race..wa.O -1.568017e-05
## absdiff.sqrt.age 3.906968e-01
## nodefactor.region.EW nodefactor.region.OW
## edges 0.39562963 0.6341524
## nodefactor.deg.main.deg.pers.0.1 0.21062606 0.3593852
## nodefactor.deg.main.deg.pers.0.2 0.08936127 0.1804954
## nodefactor.deg.main.deg.pers.1.0 0.11598603 0.1743345
## nodefactor.deg.main.deg.pers.1.1 0.20430064 0.3133835
## nodefactor.deg.main.deg.pers.1.2 0.20898824 0.3142615
## nodefactor.race..wa.B 0.11232405 0.2113809
## nodefactor.race..wa.H 0.30287788 0.2977940
## nodefactor.region.EW 1.00000000 0.1280193
## nodefactor.region.OW 0.12801927 1.0000000
## nodematch.race..wa.B 0.01363517 0.0401896
## nodematch.race..wa.H 0.13590600 0.0859614
## nodematch.race..wa.O 0.26774416 0.5194865
## absdiff.sqrt.age 0.29960318 0.4863703
## nodematch.race..wa.B nodematch.race..wa.H
## edges 0.074996149 0.160352068
## nodefactor.deg.main.deg.pers.0.1 0.056137357 0.059167966
## nodefactor.deg.main.deg.pers.0.2 0.036028348 0.041555174
## nodefactor.deg.main.deg.pers.1.0 0.018628073 0.056017259
## nodefactor.deg.main.deg.pers.1.1 0.019851724 0.106652810
## nodefactor.deg.main.deg.pers.1.2 0.019047156 0.109497662
## nodefactor.race..wa.B 0.357616998 0.000698402
## nodefactor.race..wa.H 0.002479265 0.546694257
## nodefactor.region.EW 0.013635171 0.135905997
## nodefactor.region.OW 0.040189596 0.085961398
## nodematch.race..wa.B 1.000000000 0.001499866
## nodematch.race..wa.H 0.001499866 1.000000000
## nodematch.race..wa.O -0.000613464 -0.001970742
## absdiff.sqrt.age 0.064273987 0.127626854
## nodematch.race..wa.O absdiff.sqrt.age
## edges 7.784942e-01 0.76458382
## nodefactor.deg.main.deg.pers.0.1 4.472596e-01 0.42067332
## nodefactor.deg.main.deg.pers.0.2 2.062771e-01 0.20463286
## nodefactor.deg.main.deg.pers.1.0 2.075792e-01 0.20972047
## nodefactor.deg.main.deg.pers.1.1 3.703371e-01 0.37142781
## nodefactor.deg.main.deg.pers.1.2 3.797181e-01 0.39259709
## nodefactor.race..wa.B 1.434573e-03 0.30310834
## nodefactor.race..wa.H -1.568017e-05 0.39069683
## nodefactor.region.EW 2.677442e-01 0.29960318
## nodefactor.region.OW 5.194865e-01 0.48637032
## nodematch.race..wa.B -6.134640e-04 0.06427399
## nodematch.race..wa.H -1.970742e-03 0.12762685
## nodematch.race..wa.O 1.000000e+00 0.59139290
## absdiff.sqrt.age 5.913929e-01 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 0.015639428 -0.0075381489
## Lag 2e+05 0.029739597 0.0161242635
## Lag 3e+05 0.004437324 0.0057143155
## Lag 4e+05 -0.007268947 -0.0008818797
## Lag 5e+05 0.018410690 -0.0031956244
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 -0.003088706
## Lag 2e+05 -0.008028803
## Lag 3e+05 0.009065282
## Lag 4e+05 -0.031913568
## Lag 5e+05 0.009823515
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.013191464
## Lag 2e+05 -0.002536912
## Lag 3e+05 -0.031767337
## Lag 4e+05 -0.018258020
## Lag 5e+05 0.019230389
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.0000000000
## Lag 1e+05 0.0017759450
## Lag 2e+05 0.0009240739
## Lag 3e+05 0.0005815743
## Lag 4e+05 0.0151026570
## Lag 5e+05 0.0054802639
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.0000000000
## Lag 1e+05 -0.0110423959 -0.0219456975
## Lag 2e+05 0.0006392008 -0.0004201364
## Lag 3e+05 -0.0121550463 -0.0162648282
## Lag 4e+05 0.0350538111 -0.0048677581
## Lag 5e+05 0.0068886534 -0.0007675764
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0138301862 0.013398997 -0.004006493
## Lag 2e+05 0.0007955239 0.020698867 0.004643618
## Lag 3e+05 0.0053702727 -0.011206711 0.014720305
## Lag 4e+05 0.0071200229 0.009793259 0.001856337
## Lag 5e+05 0.0037184999 -0.015013487 0.033970021
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.007002546 0.006198415 0.014962849
## Lag 2e+05 -0.006525974 -0.003613460 0.023248283
## Lag 3e+05 0.012655812 -0.016096745 -0.006757479
## Lag 4e+05 -0.002003556 -0.010960502 0.020444929
## Lag 5e+05 -0.017350162 0.020417128 0.020571621
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 0.001116059
## Lag 2e+05 -0.007936980
## Lag 3e+05 0.014579869
## Lag 4e+05 -0.018629482
## Lag 5e+05 0.006166657
## Chain 2
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 -0.0290309273 -0.039787119
## Lag 2e+05 -0.0203074970 -0.011480284
## Lag 3e+05 0.0277578548 0.025066830
## Lag 4e+05 0.0005155407 0.000111389
## Lag 5e+05 -0.0221963780 -0.005350393
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.00000000
## Lag 1e+05 -0.02896627
## Lag 2e+05 0.02796971
## Lag 3e+05 0.03552105
## Lag 4e+05 -0.01171522
## Lag 5e+05 0.01868123
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.033035884
## Lag 2e+05 0.014034214
## Lag 3e+05 0.021658058
## Lag 4e+05 -0.003103217
## Lag 5e+05 0.014882570
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.036106211
## Lag 2e+05 0.004209154
## Lag 3e+05 -0.010850066
## Lag 4e+05 0.001481208
## Lag 5e+05 -0.009647078
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.007881757 -0.003185169
## Lag 2e+05 0.012347734 -0.020194874
## Lag 3e+05 0.017318402 -0.003354750
## Lag 4e+05 -0.030852391 -0.014823966
## Lag 5e+05 -0.022482993 0.010357453
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000000 1.00000000
## Lag 1e+05 -0.004344714 -0.0002710276 -0.03659248
## Lag 2e+05 -0.012548328 0.0075237290 -0.01764495
## Lag 3e+05 0.003845897 0.0139026417 -0.01666552
## Lag 4e+05 0.004673396 -0.0073074932 -0.02680332
## Lag 5e+05 0.002696159 0.0025992264 0.01652033
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.005022363 0.005925113 -0.034540236
## Lag 2e+05 -0.012735899 0.005718384 -0.003462508
## Lag 3e+05 -0.007674051 -0.008523381 0.025757444
## Lag 4e+05 0.014134855 -0.009746605 0.009347980
## Lag 5e+05 0.014561609 0.009932555 -0.021292046
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.033429470
## Lag 2e+05 -0.009695349
## Lag 3e+05 0.007970883
## Lag 4e+05 -0.009644449
## Lag 5e+05 0.009486099
## Chain 3
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 0.006895065 -0.0038768301
## Lag 2e+05 0.011633541 -0.0172810800
## Lag 3e+05 -0.008307610 -0.0007647296
## Lag 4e+05 0.011172434 0.0177191742
## Lag 5e+05 -0.021665982 -0.0088932741
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 -0.005009565
## Lag 2e+05 0.002852452
## Lag 3e+05 0.001069893
## Lag 4e+05 -0.006614960
## Lag 5e+05 -0.007788770
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.00000000
## Lag 1e+05 -0.01669872
## Lag 2e+05 -0.01758410
## Lag 3e+05 0.01199768
## Lag 4e+05 -0.02465833
## Lag 5e+05 -0.00108392
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 -0.001694849
## Lag 2e+05 -0.007041408
## Lag 3e+05 -0.028443219
## Lag 4e+05 0.009218897
## Lag 5e+05 -0.049514358
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.006454705 0.008094175
## Lag 2e+05 0.014700586 -0.002839267
## Lag 3e+05 -0.002587302 0.001143273
## Lag 4e+05 -0.018813435 -0.016308708
## Lag 5e+05 -0.030574860 -0.008320280
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000000 1.0000000000
## Lag 1e+05 -0.011170516 0.0092333491 -0.0105373843
## Lag 2e+05 0.002729843 -0.0055320000 0.0140708141
## Lag 3e+05 0.017491271 -0.0097960779 0.0008860126
## Lag 4e+05 -0.009595410 0.0349754205 0.0163821508
## Lag 5e+05 -0.017504586 -0.0002574136 -0.0111132231
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.00000000 1.00000000 1.000000000
## Lag 1e+05 0.01536764 0.02686632 -0.017573164
## Lag 2e+05 -0.01370400 0.01146938 -0.005879545
## Lag 3e+05 -0.01019450 0.02075564 -0.011104557
## Lag 4e+05 -0.01981092 0.02309589 0.026157677
## Lag 5e+05 -0.02556634 0.01268123 -0.000635951
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 0.008354287
## Lag 2e+05 0.008883139
## Lag 3e+05 -0.003008616
## Lag 4e+05 0.018826394
## Lag 5e+05 -0.007555189
## Chain 4
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.0000000000 1.0000000000
## Lag 1e+05 -0.0001373139 -0.0187795761
## Lag 2e+05 -0.0328464665 -0.0186557594
## Lag 3e+05 0.0196746259 0.0071373997
## Lag 4e+05 0.0300349593 -0.0007255859
## Lag 5e+05 -0.0066158193 -0.0030375924
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.003514960
## Lag 2e+05 0.003992371
## Lag 3e+05 0.006030236
## Lag 4e+05 0.028936796
## Lag 5e+05 0.011158809
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.0000000000
## Lag 1e+05 -0.0201131154
## Lag 2e+05 -0.0051955928
## Lag 3e+05 -0.0174371766
## Lag 4e+05 0.0132795103
## Lag 5e+05 -0.0002295696
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.0000000000
## Lag 1e+05 0.0343485545
## Lag 2e+05 0.0258261286
## Lag 3e+05 0.0053252010
## Lag 4e+05 -0.0030384055
## Lag 5e+05 0.0001929692
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 -0.0048736327 -0.005629042
## Lag 2e+05 -0.0205322394 -0.019179812
## Lag 3e+05 -0.0005043537 -0.006951293
## Lag 4e+05 0.0267432443 0.012913563
## Lag 5e+05 -0.0091825601 0.007621813
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.019557962 0.0001740286 -0.010092181
## Lag 2e+05 -0.008694067 0.0148882388 -0.010176647
## Lag 3e+05 -0.024683145 0.0002589534 0.027198868
## Lag 4e+05 0.005262817 -0.0216157605 0.011735269
## Lag 5e+05 -0.008770376 -0.0021494300 -0.002400858
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 -0.0136555561 -0.019490391 0.017059172
## Lag 2e+05 -0.0112070332 -0.006374499 -0.032926032
## Lag 3e+05 -0.0146943340 -0.019519488 0.024855185
## Lag 4e+05 -0.0156432419 0.018666731 0.021738726
## Lag 5e+05 -0.0003011011 -0.004740596 0.007178304
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.009508579
## Lag 2e+05 0.004901388
## Lag 3e+05 0.001593036
## Lag 4e+05 0.023898882
## Lag 5e+05 -0.010446095
## Chain 5
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.005604239 0.024954060
## Lag 2e+05 0.025020051 0.026463678
## Lag 3e+05 0.014414874 0.009186940
## Lag 4e+05 -0.015216668 0.023119384
## Lag 5e+05 -0.004237965 0.004845083
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.033099346
## Lag 2e+05 -0.012422938
## Lag 3e+05 0.014287635
## Lag 4e+05 0.016379306
## Lag 5e+05 0.007842349
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.023696298
## Lag 2e+05 -0.005212683
## Lag 3e+05 0.012645741
## Lag 4e+05 0.016135019
## Lag 5e+05 0.029521874
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.007228309
## Lag 2e+05 0.012245129
## Lag 3e+05 -0.030071428
## Lag 4e+05 -0.016809195
## Lag 5e+05 -0.025309319
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 -0.0133755881 0.004252881
## Lag 2e+05 -0.0006316614 -0.018873172
## Lag 3e+05 0.0124967560 0.025786091
## Lag 4e+05 -0.0030926443 0.022327601
## Lag 5e+05 -0.0215794938 0.001026766
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.019031118 0.008349919 -0.0128186540
## Lag 2e+05 0.015778949 -0.009316558 0.0135620561
## Lag 3e+05 -0.013464751 -0.002791012 -0.0009419774
## Lag 4e+05 -0.006800818 0.009304091 -0.0134563052
## Lag 5e+05 -0.014074432 0.023585974 0.0014430338
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.0000000000 1.0000000000
## Lag 1e+05 -0.046265117 0.0120844601 0.0157247632
## Lag 2e+05 0.003431572 -0.0216573055 0.0326777588
## Lag 3e+05 0.003985561 -0.0021309770 0.0006952747
## Lag 4e+05 0.018422781 -0.0006185005 -0.0057015447
## Lag 5e+05 -0.020206237 -0.0181989141 0.0009979808
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 0.006136346
## Lag 2e+05 0.007343563
## Lag 3e+05 -0.009421597
## Lag 4e+05 -0.012758190
## Lag 5e+05 -0.006020123
## Chain 6
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 0.037840525 0.0291431022
## Lag 2e+05 -0.007980122 -0.0009526951
## Lag 3e+05 -0.002186117 -0.0181943625
## Lag 4e+05 -0.014192490 0.0250638948
## Lag 5e+05 0.011403172 0.0033216114
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.0000000000
## Lag 1e+05 -0.0107765264
## Lag 2e+05 0.0078438985
## Lag 3e+05 -0.0008727871
## Lag 4e+05 -0.0053017389
## Lag 5e+05 -0.0290987013
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.008305480
## Lag 2e+05 -0.003069145
## Lag 3e+05 -0.027181482
## Lag 4e+05 -0.025797107
## Lag 5e+05 -0.002132888
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.011000145
## Lag 2e+05 -0.010774679
## Lag 3e+05 0.014422446
## Lag 4e+05 -0.003528089
## Lag 5e+05 -0.016561240
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.00560792 0.014302886
## Lag 2e+05 0.01837006 -0.004619584
## Lag 3e+05 -0.02901384 0.009385448
## Lag 4e+05 0.00756092 0.013721063
## Lag 5e+05 0.01549756 -0.006721578
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.011441087 0.021936568 0.004116709
## Lag 2e+05 -0.011396975 -0.005897103 0.001635803
## Lag 3e+05 0.003639141 0.003283407 0.015200212
## Lag 4e+05 -0.021862893 -0.015692110 -0.017249396
## Lag 5e+05 0.002734913 0.020993150 -0.008730709
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.002961158 0.017331638 0.022400686
## Lag 2e+05 -0.004279574 0.003808839 0.010695344
## Lag 3e+05 -0.007098422 0.023744883 0.036053335
## Lag 4e+05 -0.034010399 -0.007873665 -0.008788388
## Lag 5e+05 0.008476088 -0.009711478 -0.004177706
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 0.012993728
## Lag 2e+05 -0.024825080
## Lag 3e+05 -0.021574466
## Lag 4e+05 -0.005599627
## Lag 5e+05 0.005403102
## Chain 7
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 -0.007901092 -0.0128422155
## Lag 2e+05 0.013150909 -0.0019509213
## Lag 3e+05 0.009631403 -0.0006970397
## Lag 4e+05 -0.027800953 -0.0044496533
## Lag 5e+05 -0.020390202 -0.0127240668
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 -0.019707210
## Lag 2e+05 -0.018688493
## Lag 3e+05 -0.013684223
## Lag 4e+05 -0.001928193
## Lag 5e+05 -0.005117483
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.013329969
## Lag 2e+05 0.012331323
## Lag 3e+05 0.026714637
## Lag 4e+05 -0.002936256
## Lag 5e+05 -0.015961127
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.005756863
## Lag 2e+05 0.005358271
## Lag 3e+05 0.008943830
## Lag 4e+05 -0.003785219
## Lag 5e+05 0.013954168
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 0.0003420731 -0.006497138
## Lag 2e+05 -0.0056926857 -0.001744728
## Lag 3e+05 0.0126847586 0.006471145
## Lag 4e+05 -0.0080426682 -0.015906986
## Lag 5e+05 -0.0085963466 -0.024640889
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.012618056 0.007941287 -0.003263804
## Lag 2e+05 0.014306991 -0.006917664 0.004609770
## Lag 3e+05 -0.005747867 -0.011066343 -0.008806503
## Lag 4e+05 -0.012314578 -0.020711298 0.018731682
## Lag 5e+05 -0.005871468 -0.001652041 -0.001200922
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.002603057 -0.0011249083 -0.010077286
## Lag 2e+05 0.003959600 -0.0001576194 0.017815664
## Lag 3e+05 0.010979266 -0.0264310211 0.005596769
## Lag 4e+05 -0.018666901 0.0119213240 -0.001692823
## Lag 5e+05 0.018920308 0.0221700270 0.011709217
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.017543734
## Lag 2e+05 0.023846321
## Lag 3e+05 0.002608732
## Lag 4e+05 -0.033589301
## Lag 5e+05 -0.026674610
## Chain 8
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.021138378 0.020353901
## Lag 2e+05 0.008556820 -0.001831348
## Lag 3e+05 0.013467476 -0.003579938
## Lag 4e+05 -0.019258016 -0.013229369
## Lag 5e+05 0.007876562 0.003433621
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 -0.013899941
## Lag 2e+05 0.020543135
## Lag 3e+05 0.002466346
## Lag 4e+05 0.004410165
## Lag 5e+05 0.010126699
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.002087121
## Lag 2e+05 -0.030978972
## Lag 3e+05 0.003617391
## Lag 4e+05 0.008572298
## Lag 5e+05 0.003095679
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 -0.014227811
## Lag 2e+05 -0.016187573
## Lag 3e+05 0.013481054
## Lag 4e+05 0.008353760
## Lag 5e+05 -0.007196712
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.006306943 0.001559647
## Lag 2e+05 -0.001843008 0.015856349
## Lag 3e+05 0.020493279 -0.006634703
## Lag 4e+05 -0.039019520 -0.012671677
## Lag 5e+05 -0.013091068 -0.001168916
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.003929975 0.008552509 0.027299650
## Lag 2e+05 0.002041159 0.003940562 0.009809759
## Lag 3e+05 -0.012792845 0.012676536 -0.010920789
## Lag 4e+05 0.007043652 0.001140703 -0.001763095
## Lag 5e+05 -0.015995017 0.018210158 0.004851399
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000e+00 1.000000000
## Lag 1e+05 -0.024497069 2.036954e-03 0.020094760
## Lag 2e+05 0.020993021 4.605290e-06 -0.021298149
## Lag 3e+05 -0.002666515 -5.771838e-04 0.020303452
## Lag 4e+05 -0.004576363 2.935633e-02 -0.001221739
## Lag 5e+05 -0.013146089 -9.281761e-03 -0.016015179
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 0.019121829
## Lag 2e+05 0.012735967
## Lag 3e+05 -0.027887779
## Lag 4e+05 -0.025727528
## Lag 5e+05 0.001179783
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.28720 -1.31474
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -1.19983 -0.08850
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 1.23551 -1.59674
## nodefactor.race..wa.B nodefactor.race..wa.H
## 1.11538 -0.07941
## nodefactor.region.EW nodefactor.region.OW
## -0.78435 -0.77976
## nodematch.race..wa.B nodematch.race..wa.H
## 1.52970 0.03975
## nodematch.race..wa.O absdiff.sqrt.age
## -0.28387 -0.37169
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.7739628 0.1885964
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.2302042 0.9294773
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.2166402 0.1103244
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.2646881 0.9367100
## nodefactor.region.EW nodefactor.region.OW
## 0.4328334 0.4355347
## nodematch.race..wa.B nodematch.race..wa.H
## 0.1260912 0.9682958
## nodematch.race..wa.O absdiff.sqrt.age
## 0.7765076 0.7101229
## Joint P-value (lower = worse): 0.9047583 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.829015 -0.411410
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -1.527001 -0.009976
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.606915 -0.806512
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.151949 -0.875258
## nodefactor.region.EW nodefactor.region.OW
## -0.578872 -0.436462
## nodematch.race..wa.B nodematch.race..wa.H
## 0.751316 0.090636
## nodematch.race..wa.O absdiff.sqrt.age
## -0.070677 -0.730110
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.4070957 0.6807718
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.1267608 0.9920404
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.5439072 0.4199477
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.8792276 0.3814337
## nodefactor.region.EW nodefactor.region.OW
## 0.5626754 0.6625017
## nodematch.race..wa.B nodematch.race..wa.H
## 0.4524622 0.9277821
## nodematch.race..wa.O absdiff.sqrt.age
## 0.9436547 0.4653229
## Joint P-value (lower = worse): 0.9680146 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.40194 -0.01595
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 1.72404 1.75124
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.37167 0.86400
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.44226 -1.73219
## nodefactor.region.EW nodefactor.region.OW
## 0.10679 0.70563
## nodematch.race..wa.B nodematch.race..wa.H
## 0.19376 -0.07675
## nodematch.race..wa.O absdiff.sqrt.age
## 1.49390 0.81522
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.68772906 0.98727082
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.08470144 0.07990390
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.71013664 0.38758913
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.65829897 0.08323924
## nodefactor.region.EW nodefactor.region.OW
## 0.91495670 0.48041582
## nodematch.race..wa.B nodematch.race..wa.H
## 0.84636769 0.93882221
## nodematch.race..wa.O absdiff.sqrt.age
## 0.13520292 0.41494443
## Joint P-value (lower = worse): 0.5940885 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.4184 -0.1290
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.3174 -0.3168
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -1.2190 1.2427
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.4139 -1.0501
## nodefactor.region.EW nodefactor.region.OW
## -1.9051 0.5751
## nodematch.race..wa.B nodematch.race..wa.H
## 0.4331 -1.4620
## nodematch.race..wa.O absdiff.sqrt.age
## 0.4120 -1.1598
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.67567211 0.89733231
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.75091388 0.75136556
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.22282572 0.21396974
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.67895191 0.29365049
## nodefactor.region.EW nodefactor.region.OW
## 0.05676442 0.56520890
## nodematch.race..wa.B nodematch.race..wa.H
## 0.66492645 0.14372965
## nodematch.race..wa.O absdiff.sqrt.age
## 0.68033499 0.24614527
## Joint P-value (lower = worse): 0.6646768 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.27805 -0.12605
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.94053 2.83070
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -1.36660 -0.08537
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.21573 -0.89710
## nodefactor.region.EW nodefactor.region.OW
## 2.35538 -1.12601
## nodematch.race..wa.B nodematch.race..wa.H
## 0.22372 0.29679
## nodematch.race..wa.O absdiff.sqrt.age
## 0.74658 0.65210
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.780975482 0.899693310
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.346945246 0.004644651
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.171749704 0.931967679
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.829197425 0.369664846
## nodefactor.region.EW nodefactor.region.OW
## 0.018503787 0.260161965
## nodematch.race..wa.B nodematch.race..wa.H
## 0.822972775 0.766628497
## nodematch.race..wa.O absdiff.sqrt.age
## 0.455320041 0.514334193
## Joint P-value (lower = worse): 0.3632008 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 2.2718 1.9141
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 1.4310 0.6521
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.9718 1.4300
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.5751 1.5276
## nodefactor.region.EW nodefactor.region.OW
## 1.2643 0.6554
## nodematch.race..wa.B nodematch.race..wa.H
## -0.1282 1.5610
## nodematch.race..wa.O absdiff.sqrt.age
## 2.6162 0.5426
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.023097780 0.055607649
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.152439262 0.514307403
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.331163232 0.152725477
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.565200383 0.126618330
## nodefactor.region.EW nodefactor.region.OW
## 0.206121583 0.512222705
## nodematch.race..wa.B nodematch.race..wa.H
## 0.897999785 0.118516933
## nodematch.race..wa.O absdiff.sqrt.age
## 0.008891021 0.587392959
## Joint P-value (lower = worse): 0.6223076 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.20650 -0.06224
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.36688 -0.65690
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.11850 -0.79751
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.67853 -1.86867
## nodefactor.region.EW nodefactor.region.OW
## -0.19532 -0.04435
## nodematch.race..wa.B nodematch.race..wa.H
## -2.21051 -1.78936
## nodematch.race..wa.O absdiff.sqrt.age
## 0.92439 -0.28279
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.83639991 0.95036835
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.71371045 0.51124742
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.90567205 0.42515264
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.49743836 0.06166879
## nodefactor.region.EW nodefactor.region.OW
## 0.84514572 0.96462535
## nodematch.race..wa.B nodematch.race..wa.H
## 0.02706964 0.07355687
## nodematch.race..wa.O absdiff.sqrt.age
## 0.35528255 0.77733421
## Joint P-value (lower = worse): 0.9164804 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.5203 -0.2522
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.3955 1.7603
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.7848 0.6090
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.1761 -0.6157
## nodefactor.region.EW nodefactor.region.OW
## -0.7046 0.9444
## nodematch.race..wa.B nodematch.race..wa.H
## 0.7474 0.4555
## nodematch.race..wa.O absdiff.sqrt.age
## 0.9814 -0.7766
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.6028833 0.8008482
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.6924599 0.0783584
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.4325754 0.5425482
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.8602167 0.5380992
## nodefactor.region.EW nodefactor.region.OW
## 0.4810359 0.3449738
## nodematch.race..wa.B nodematch.race..wa.H
## 0.4548377 0.6487240
## nodematch.race..wa.O absdiff.sqrt.age
## 0.3264055 0.4374197
## Joint P-value (lower = worse): 0.7827664 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 7
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 1.551413 21.896 0.126416 0.14822
## nodefactor.deg.main.deg.pers.0.1 0.419227 14.240 0.082212 0.10988
## nodefactor.deg.main.deg.pers.0.2 0.133133 6.181 0.035685 0.03628
## nodefactor.deg.main.deg.pers.1.0 0.008829 6.315 0.036460 0.03640
## nodefactor.deg.main.deg.pers.1.1 -0.101231 12.392 0.071544 0.09784
## nodefactor.deg.main.deg.pers.1.2 -0.103491 12.951 0.074772 0.10293
## nodefactor.riskg.O3 0.020816 2.642 0.015252 0.01532
## nodefactor.riskg.O4 1.208370 11.716 0.067645 0.07400
## nodefactor.riskg.Y2 -0.047343 2.867 0.016555 0.01667
## nodefactor.riskg.Y3 -0.074032 8.699 0.050223 0.04970
## nodefactor.race..wa.B 0.416284 9.004 0.051983 0.06790
## nodefactor.race..wa.H 0.666647 13.349 0.077070 0.11407
## nodefactor.region.EW 0.932858 9.604 0.055450 0.06457
## nodefactor.region.OW 0.590727 17.467 0.100846 0.11520
## nodematch.race..wa.B 0.190249 1.658 0.009572 0.01229
## nodematch.race..wa.H 0.199580 3.668 0.021178 0.03516
## nodematch.race..wa.O 0.835044 16.971 0.097982 0.11278
## absdiff.sqrt.age 2.550655 22.497 0.129886 0.13852
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -40.159 -13.1586 0.84138 15.841 44.841
## nodefactor.deg.main.deg.pers.0.1 -26.310 -9.3100 -0.31004 9.690 28.715
## nodefactor.deg.main.deg.pers.0.2 -11.371 -4.3710 -0.37103 4.629 12.629
## nodefactor.deg.main.deg.pers.1.0 -12.033 -4.0335 -0.03347 3.967 12.967
## nodefactor.deg.main.deg.pers.1.1 -23.538 -8.5379 -0.53786 8.462 25.462
## nodefactor.deg.main.deg.pers.1.2 -24.388 -9.3881 -0.38812 8.612 25.612
## nodefactor.riskg.O3 -4.856 -1.8558 0.14418 2.144 5.144
## nodefactor.riskg.O4 -20.513 -6.5127 1.48734 8.487 24.487
## nodefactor.riskg.Y2 -5.202 -2.2024 -0.20238 1.798 5.798
## nodefactor.riskg.Y3 -16.786 -5.7860 0.21403 6.214 17.214
## nodefactor.race..wa.B -16.591 -5.5908 0.40918 6.409 18.409
## nodefactor.race..wa.H -25.174 -8.1739 0.82608 9.826 27.826
## nodefactor.region.EW -17.501 -5.5014 0.49862 7.499 20.499
## nodefactor.region.OW -32.486 -11.4862 0.51379 12.514 35.514
## nodematch.race..wa.B -2.540 -0.5399 0.46015 1.460 3.460
## nodematch.race..wa.H -6.269 -2.2690 -0.26902 2.731 7.731
## nodematch.race..wa.O -31.880 -10.8800 1.11998 12.120 35.120
## absdiff.sqrt.age -40.664 -12.8877 2.17723 17.676 47.694
##
##
## Sample statistics cross-correlations:
## edges
## edges 1.00000000
## nodefactor.deg.main.deg.pers.0.1 0.55079177
## nodefactor.deg.main.deg.pers.0.2 0.27985803
## nodefactor.deg.main.deg.pers.1.0 0.28363095
## nodefactor.deg.main.deg.pers.1.1 0.49715069
## nodefactor.deg.main.deg.pers.1.2 0.51273448
## nodefactor.riskg.O3 0.11468086
## nodefactor.riskg.O4 0.42745017
## nodefactor.riskg.Y2 0.12482481
## nodefactor.riskg.Y3 0.37591674
## nodefactor.race..wa.B 0.37239944
## nodefactor.race..wa.H 0.51428535
## nodefactor.region.EW 0.40133906
## nodefactor.region.OW 0.63523544
## nodematch.race..wa.B 0.07862664
## nodematch.race..wa.H 0.17011344
## nodematch.race..wa.O 0.77359501
## absdiff.sqrt.age 0.77440939
## nodefactor.deg.main.deg.pers.0.1
## edges 0.55079177
## nodefactor.deg.main.deg.pers.0.1 1.00000000
## nodefactor.deg.main.deg.pers.0.2 0.08083147
## nodefactor.deg.main.deg.pers.1.0 0.08411097
## nodefactor.deg.main.deg.pers.1.1 0.13805665
## nodefactor.deg.main.deg.pers.1.2 0.13935626
## nodefactor.riskg.O3 0.06548722
## nodefactor.riskg.O4 0.24042426
## nodefactor.riskg.Y2 0.06056040
## nodefactor.riskg.Y3 0.19452421
## nodefactor.race..wa.B 0.25014081
## nodefactor.race..wa.H 0.21413937
## nodefactor.region.EW 0.19988488
## nodefactor.region.OW 0.38818301
## nodematch.race..wa.B 0.05509942
## nodematch.race..wa.H 0.04274979
## nodematch.race..wa.O 0.44710570
## absdiff.sqrt.age 0.42841762
## nodefactor.deg.main.deg.pers.0.2
## edges 0.27985803
## nodefactor.deg.main.deg.pers.0.1 0.08083147
## nodefactor.deg.main.deg.pers.0.2 1.00000000
## nodefactor.deg.main.deg.pers.1.0 0.04203955
## nodefactor.deg.main.deg.pers.1.1 0.07621989
## nodefactor.deg.main.deg.pers.1.2 0.07708548
## nodefactor.riskg.O3 0.02533878
## nodefactor.riskg.O4 0.12092259
## nodefactor.riskg.Y2 0.03717978
## nodefactor.riskg.Y3 0.11013557
## nodefactor.race..wa.B 0.11745126
## nodefactor.race..wa.H 0.14247019
## nodefactor.region.EW 0.08448266
## nodefactor.region.OW 0.17505361
## nodematch.race..wa.B 0.02775291
## nodematch.race..wa.H 0.04654421
## nodematch.race..wa.O 0.21172315
## absdiff.sqrt.age 0.21719572
## nodefactor.deg.main.deg.pers.1.0
## edges 0.283630946
## nodefactor.deg.main.deg.pers.0.1 0.084110972
## nodefactor.deg.main.deg.pers.0.2 0.042039546
## nodefactor.deg.main.deg.pers.1.0 1.000000000
## nodefactor.deg.main.deg.pers.1.1 0.073465047
## nodefactor.deg.main.deg.pers.1.2 0.069585160
## nodefactor.riskg.O3 0.034538180
## nodefactor.riskg.O4 0.128899376
## nodefactor.riskg.Y2 0.032027043
## nodefactor.riskg.Y3 0.101567284
## nodefactor.race..wa.B 0.089414751
## nodefactor.race..wa.H 0.175407892
## nodefactor.region.EW 0.126322910
## nodefactor.region.OW 0.174718646
## nodematch.race..wa.B 0.004013376
## nodematch.race..wa.H 0.064794176
## nodematch.race..wa.O 0.207849361
## absdiff.sqrt.age 0.218926028
## nodefactor.deg.main.deg.pers.1.1
## edges 0.49715069
## nodefactor.deg.main.deg.pers.0.1 0.13805665
## nodefactor.deg.main.deg.pers.0.2 0.07621989
## nodefactor.deg.main.deg.pers.1.0 0.07346505
## nodefactor.deg.main.deg.pers.1.1 1.00000000
## nodefactor.deg.main.deg.pers.1.2 0.13527626
## nodefactor.riskg.O3 0.06463869
## nodefactor.riskg.O4 0.19285052
## nodefactor.riskg.Y2 0.06311208
## nodefactor.riskg.Y3 0.19695141
## nodefactor.race..wa.B 0.16784921
## nodefactor.race..wa.H 0.28731343
## nodefactor.region.EW 0.18946314
## nodefactor.region.OW 0.32599534
## nodematch.race..wa.B 0.03654982
## nodematch.race..wa.H 0.10700459
## nodematch.race..wa.O 0.37335186
## absdiff.sqrt.age 0.37185967
## nodefactor.deg.main.deg.pers.1.2
## edges 0.51273448
## nodefactor.deg.main.deg.pers.0.1 0.13935626
## nodefactor.deg.main.deg.pers.0.2 0.07708548
## nodefactor.deg.main.deg.pers.1.0 0.06958516
## nodefactor.deg.main.deg.pers.1.1 0.13527626
## nodefactor.deg.main.deg.pers.1.2 1.00000000
## nodefactor.riskg.O3 0.05661469
## nodefactor.riskg.O4 0.21560734
## nodefactor.riskg.Y2 0.08141334
## nodefactor.riskg.Y3 0.20139001
## nodefactor.race..wa.B 0.12180411
## nodefactor.race..wa.H 0.32417273
## nodefactor.region.EW 0.24525850
## nodefactor.region.OW 0.30125716
## nodematch.race..wa.B 0.01553903
## nodematch.race..wa.H 0.13192455
## nodematch.race..wa.O 0.38929938
## absdiff.sqrt.age 0.39501466
## nodefactor.riskg.O3 nodefactor.riskg.O4
## edges 0.114680858 0.42745017
## nodefactor.deg.main.deg.pers.0.1 0.065487218 0.24042426
## nodefactor.deg.main.deg.pers.0.2 0.025338778 0.12092259
## nodefactor.deg.main.deg.pers.1.0 0.034538180 0.12889938
## nodefactor.deg.main.deg.pers.1.1 0.064638685 0.19285052
## nodefactor.deg.main.deg.pers.1.2 0.056614689 0.21560734
## nodefactor.riskg.O3 1.000000000 0.05078592
## nodefactor.riskg.O4 0.050785917 1.00000000
## nodefactor.riskg.Y2 0.002951720 0.02254111
## nodefactor.riskg.Y3 0.018259488 0.05964692
## nodefactor.race..wa.B 0.033601211 0.17516599
## nodefactor.race..wa.H 0.061804666 0.26532361
## nodefactor.region.EW 0.045782430 0.16608973
## nodefactor.region.OW 0.073539239 0.27847081
## nodematch.race..wa.B 0.007178089 0.03422864
## nodematch.race..wa.H 0.018634883 0.10602910
## nodematch.race..wa.O 0.090137441 0.29839430
## absdiff.sqrt.age 0.116451900 0.43038125
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## edges 0.124824807 0.37591674
## nodefactor.deg.main.deg.pers.0.1 0.060560405 0.19452421
## nodefactor.deg.main.deg.pers.0.2 0.037179782 0.11013557
## nodefactor.deg.main.deg.pers.1.0 0.032027043 0.10156728
## nodefactor.deg.main.deg.pers.1.1 0.063112075 0.19695141
## nodefactor.deg.main.deg.pers.1.2 0.081413338 0.20139001
## nodefactor.riskg.O3 0.002951720 0.01825949
## nodefactor.riskg.O4 0.022541108 0.05964692
## nodefactor.riskg.Y2 1.000000000 0.01644260
## nodefactor.riskg.Y3 0.016442599 1.00000000
## nodefactor.race..wa.B 0.046047909 0.12697607
## nodefactor.race..wa.H 0.073718183 0.18581141
## nodefactor.region.EW 0.051822298 0.15067833
## nodefactor.region.OW 0.077282826 0.23338907
## nodematch.race..wa.B -0.003482857 0.01781101
## nodematch.race..wa.H 0.028923158 0.06137097
## nodematch.race..wa.O 0.090299049 0.30057440
## absdiff.sqrt.age 0.091658867 0.28221988
## nodefactor.race..wa.B
## edges 0.372399440
## nodefactor.deg.main.deg.pers.0.1 0.250140813
## nodefactor.deg.main.deg.pers.0.2 0.117451258
## nodefactor.deg.main.deg.pers.1.0 0.089414751
## nodefactor.deg.main.deg.pers.1.1 0.167849213
## nodefactor.deg.main.deg.pers.1.2 0.121804107
## nodefactor.riskg.O3 0.033601211
## nodefactor.riskg.O4 0.175165990
## nodefactor.riskg.Y2 0.046047909
## nodefactor.riskg.Y3 0.126976070
## nodefactor.race..wa.B 1.000000000
## nodefactor.race..wa.H 0.122780010
## nodefactor.region.EW 0.108802884
## nodefactor.region.OW 0.189664105
## nodematch.race..wa.B 0.373048675
## nodematch.race..wa.H -0.008226545
## nodematch.race..wa.O -0.007309730
## absdiff.sqrt.age 0.290756194
## nodefactor.race..wa.H
## edges 0.514285349
## nodefactor.deg.main.deg.pers.0.1 0.214139374
## nodefactor.deg.main.deg.pers.0.2 0.142470192
## nodefactor.deg.main.deg.pers.1.0 0.175407892
## nodefactor.deg.main.deg.pers.1.1 0.287313425
## nodefactor.deg.main.deg.pers.1.2 0.324172726
## nodefactor.riskg.O3 0.061804666
## nodefactor.riskg.O4 0.265323609
## nodefactor.riskg.Y2 0.073718183
## nodefactor.riskg.Y3 0.185811407
## nodefactor.race..wa.B 0.122780010
## nodefactor.race..wa.H 1.000000000
## nodefactor.region.EW 0.312281974
## nodefactor.region.OW 0.280484856
## nodematch.race..wa.B -0.001287749
## nodematch.race..wa.H 0.555302439
## nodematch.race..wa.O 0.001677746
## absdiff.sqrt.age 0.402152227
## nodefactor.region.EW nodefactor.region.OW
## edges 0.401339060 0.63523544
## nodefactor.deg.main.deg.pers.0.1 0.199884875 0.38818301
## nodefactor.deg.main.deg.pers.0.2 0.084482659 0.17505361
## nodefactor.deg.main.deg.pers.1.0 0.126322910 0.17471865
## nodefactor.deg.main.deg.pers.1.1 0.189463136 0.32599534
## nodefactor.deg.main.deg.pers.1.2 0.245258502 0.30125716
## nodefactor.riskg.O3 0.045782430 0.07353924
## nodefactor.riskg.O4 0.166089730 0.27847081
## nodefactor.riskg.Y2 0.051822298 0.07728283
## nodefactor.riskg.Y3 0.150678328 0.23338907
## nodefactor.race..wa.B 0.108802884 0.18966411
## nodefactor.race..wa.H 0.312281974 0.28048486
## nodefactor.region.EW 1.000000000 0.12154771
## nodefactor.region.OW 0.121547706 1.00000000
## nodematch.race..wa.B 0.009163413 0.03164809
## nodematch.race..wa.H 0.132339507 0.08409190
## nodematch.race..wa.O 0.263771580 0.53652501
## absdiff.sqrt.age 0.305178000 0.48271453
## nodematch.race..wa.B nodematch.race..wa.H
## edges 0.078626638 0.170113440
## nodefactor.deg.main.deg.pers.0.1 0.055099416 0.042749793
## nodefactor.deg.main.deg.pers.0.2 0.027752907 0.046544214
## nodefactor.deg.main.deg.pers.1.0 0.004013376 0.064794176
## nodefactor.deg.main.deg.pers.1.1 0.036549820 0.107004588
## nodefactor.deg.main.deg.pers.1.2 0.015539029 0.131924545
## nodefactor.riskg.O3 0.007178089 0.018634883
## nodefactor.riskg.O4 0.034228640 0.106029100
## nodefactor.riskg.Y2 -0.003482857 0.028923158
## nodefactor.riskg.Y3 0.017811007 0.061370969
## nodefactor.race..wa.B 0.373048675 -0.008226545
## nodefactor.race..wa.H -0.001287749 0.555302439
## nodefactor.region.EW 0.009163413 0.132339507
## nodefactor.region.OW 0.031648093 0.084091905
## nodematch.race..wa.B 1.000000000 -0.006450259
## nodematch.race..wa.H -0.006450259 1.000000000
## nodematch.race..wa.O 0.001929891 0.001497485
## absdiff.sqrt.age 0.061812679 0.131601166
## nodematch.race..wa.O absdiff.sqrt.age
## edges 0.773595014 0.77440939
## nodefactor.deg.main.deg.pers.0.1 0.447105704 0.42841762
## nodefactor.deg.main.deg.pers.0.2 0.211723152 0.21719572
## nodefactor.deg.main.deg.pers.1.0 0.207849361 0.21892603
## nodefactor.deg.main.deg.pers.1.1 0.373351859 0.37185967
## nodefactor.deg.main.deg.pers.1.2 0.389299376 0.39501466
## nodefactor.riskg.O3 0.090137441 0.11645190
## nodefactor.riskg.O4 0.298394300 0.43038125
## nodefactor.riskg.Y2 0.090299049 0.09165887
## nodefactor.riskg.Y3 0.300574400 0.28221988
## nodefactor.race..wa.B -0.007309730 0.29075619
## nodefactor.race..wa.H 0.001677746 0.40215223
## nodefactor.region.EW 0.263771580 0.30517800
## nodefactor.region.OW 0.536525005 0.48271453
## nodematch.race..wa.B 0.001929891 0.06181268
## nodematch.race..wa.H 0.001497485 0.13160117
## nodematch.race..wa.O 1.000000000 0.59632515
## absdiff.sqrt.age 0.596325146 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 0.123245833 0.24800695
## Lag 2e+05 0.046870328 0.10857276
## Lag 3e+05 0.026452153 0.05382357
## Lag 4e+05 -0.008684585 0.02101100
## Lag 5e+05 -0.004726233 0.02159682
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.00000000
## Lag 1e+05 0.02416564
## Lag 2e+05 0.02069764
## Lag 3e+05 -0.00389325
## Lag 4e+05 -0.02110662
## Lag 5e+05 -0.02940964
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.0000000000
## Lag 1e+05 0.0087516356
## Lag 2e+05 -0.0004742542
## Lag 3e+05 0.0088293490
## Lag 4e+05 -0.0049236004
## Lag 5e+05 0.0028951513
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.21782606
## Lag 2e+05 0.10764522
## Lag 3e+05 0.08430781
## Lag 4e+05 0.05170285
## Lag 5e+05 0.02074778
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O3
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.248446954 -0.006347544
## Lag 2e+05 0.121576418 -0.030049606
## Lag 3e+05 0.070816750 0.002415947
## Lag 4e+05 0.038911689 -0.001036293
## Lag 5e+05 -0.001616253 0.004866287
## nodefactor.riskg.O4 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0 1.000000000 1.0000000000 1.0000000000
## Lag 1e+05 0.066710319 0.0013868523 -0.0131090310
## Lag 2e+05 0.025726856 0.0009062492 0.0202971529
## Lag 3e+05 0.002660957 -0.0241959682 -0.0135619196
## Lag 4e+05 0.013992293 -0.0051456530 0.0054744037
## Lag 5e+05 -0.029364955 0.0027193701 -0.0009242468
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.170597873 0.24186789 0.095311762
## Lag 2e+05 0.070733873 0.12295270 0.045694114
## Lag 3e+05 0.045379900 0.07773534 0.020718961
## Lag 4e+05 0.023254784 0.02210548 0.011640553
## Lag 5e+05 -0.001897973 0.04024069 0.008141718
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.103920129 0.20596672 0.33548573
## Lag 2e+05 0.013802601 0.08618305 0.19182749
## Lag 3e+05 -0.025875110 0.02098423 0.12130230
## Lag 4e+05 -0.009746492 0.03241847 0.05499597
## Lag 5e+05 -0.013232130 0.01380033 0.05315805
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 0.123830731 0.06072332
## Lag 2e+05 0.019024539 0.02525327
## Lag 3e+05 0.015227993 -0.00337865
## Lag 4e+05 -0.007478674 -0.02161288
## Lag 5e+05 -0.009288200 -0.01810498
## Chain 2
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.0000000000 1.00000000
## Lag 1e+05 0.1610350193 0.22435393
## Lag 2e+05 0.0572814021 0.11513577
## Lag 3e+05 0.0320105449 0.07549491
## Lag 4e+05 -0.0009286636 0.03533366
## Lag 5e+05 0.0070893378 0.01611871
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.027172414
## Lag 2e+05 0.010232045
## Lag 3e+05 -0.019801252
## Lag 4e+05 -0.031336872
## Lag 5e+05 -0.004015089
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.0000000000
## Lag 1e+05 0.0161856443
## Lag 2e+05 0.0001069805
## Lag 3e+05 -0.0231410463
## Lag 4e+05 -0.0078074880
## Lag 5e+05 0.0215900601
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.25300287
## Lag 2e+05 0.09601969
## Lag 3e+05 0.03935970
## Lag 4e+05 0.04112870
## Lag 5e+05 0.01333916
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O3
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.24240021 0.041662608
## Lag 2e+05 0.11850262 0.025405389
## Lag 3e+05 0.05482208 0.006474406
## Lag 4e+05 0.01996231 -0.007867317
## Lag 5e+05 0.01411700 -0.003991758
## nodefactor.riskg.O4 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.051940021 -0.009789975 -0.003179577
## Lag 2e+05 0.039739624 -0.020562410 0.003423918
## Lag 3e+05 0.003468693 0.009743931 0.028722165
## Lag 4e+05 0.025806080 -0.009758638 0.005794442
## Lag 5e+05 0.004996162 0.007011462 -0.006353989
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.000000000
## Lag 1e+05 0.18390284 0.22461124 0.118126001
## Lag 2e+05 0.10579155 0.10284306 0.006027581
## Lag 3e+05 0.07440813 0.04858804 0.001043890
## Lag 4e+05 0.02767747 0.04537874 0.011787779
## Lag 5e+05 0.01847206 0.02157661 -0.006509702
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.131030980 0.172495781 0.31905435
## Lag 2e+05 0.048651206 0.076487261 0.15563427
## Lag 3e+05 0.032892552 0.021061321 0.09577794
## Lag 4e+05 -0.007977758 0.016481893 0.06373729
## Lag 5e+05 -0.010671105 0.009336264 0.04127646
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.129161177 0.059528475
## Lag 2e+05 0.027332606 0.016354335
## Lag 3e+05 0.023170935 -0.019169320
## Lag 4e+05 -0.006566014 -0.008201786
## Lag 5e+05 0.008384330 0.003456119
## Chain 3
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 0.142355042 0.23795789
## Lag 2e+05 0.038362703 0.10207609
## Lag 3e+05 0.031015468 0.04895570
## Lag 4e+05 -0.001053687 0.01271543
## Lag 5e+05 0.014059458 -0.01160997
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.028057804
## Lag 2e+05 -0.003742006
## Lag 3e+05 0.002598684
## Lag 4e+05 0.014984398
## Lag 5e+05 -0.017902204
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.005130196
## Lag 2e+05 0.013781718
## Lag 3e+05 0.016086675
## Lag 4e+05 0.005821366
## Lag 5e+05 0.004143251
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.24058274
## Lag 2e+05 0.08809266
## Lag 3e+05 0.07364258
## Lag 4e+05 0.03274094
## Lag 5e+05 0.03195202
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O3
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.24581954 -0.021052138
## Lag 2e+05 0.09921817 -0.004638132
## Lag 3e+05 0.06383207 0.003778198
## Lag 4e+05 0.02825777 0.003870140
## Lag 5e+05 0.04464453 0.014634347
## nodefactor.riskg.O4 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 0.08998512 -0.010222759 -0.01960147
## Lag 2e+05 0.02564090 -0.015634864 -0.01569075
## Lag 3e+05 0.01597810 -0.018629755 0.01373665
## Lag 4e+05 0.01139190 -0.018118400 -0.02766907
## Lag 5e+05 -0.02218312 -0.005953613 0.03823502
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.154511513 0.27630724 0.11518633
## Lag 2e+05 0.075069882 0.14756169 0.07721487
## Lag 3e+05 0.047809949 0.09741470 0.03311641
## Lag 4e+05 -0.003289662 0.05379211 -0.01209963
## Lag 5e+05 -0.007624650 0.04374176 0.01359326
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.084154894 0.16185503 0.32373668
## Lag 2e+05 0.026780046 0.08798080 0.19858683
## Lag 3e+05 0.027807001 0.03233725 0.11948845
## Lag 4e+05 -0.010732458 0.01860337 0.08835310
## Lag 5e+05 0.008596883 0.02477156 0.06322743
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.10253760 0.069324029
## Lag 2e+05 0.01277395 0.014375316
## Lag 3e+05 0.01665993 0.003268780
## Lag 4e+05 0.02836650 -0.024841298
## Lag 5e+05 0.01458525 0.009135518
## Chain 4
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 0.122128015 0.21667641
## Lag 2e+05 0.042606418 0.08761656
## Lag 3e+05 0.023317945 0.04193086
## Lag 4e+05 -0.007777635 0.03207304
## Lag 5e+05 0.018560224 0.02357198
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.055681567
## Lag 2e+05 0.006229900
## Lag 3e+05 0.009811765
## Lag 4e+05 -0.027102849
## Lag 5e+05 -0.014119517
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.003456092
## Lag 2e+05 -0.001504615
## Lag 3e+05 -0.012495630
## Lag 4e+05 -0.011226278
## Lag 5e+05 -0.003669039
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.25820665
## Lag 2e+05 0.13685028
## Lag 3e+05 0.06929471
## Lag 4e+05 0.03672856
## Lag 5e+05 0.01947542
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O3
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.22196248 -0.004428586
## Lag 2e+05 0.09734995 -0.025247719
## Lag 3e+05 0.03918683 0.003013129
## Lag 4e+05 0.02390704 0.000402376
## Lag 5e+05 0.01065633 0.000788497
## nodefactor.riskg.O4 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0 1.0000000000 1.00000000 1.000000000
## Lag 1e+05 0.0723691690 0.01429688 -0.009108107
## Lag 2e+05 0.0094687669 0.03680894 0.004120924
## Lag 3e+05 0.0001410807 -0.01637405 -0.022674064
## Lag 4e+05 -0.0237502286 0.01461509 0.008190934
## Lag 5e+05 0.0002956996 -0.01562636 0.003587994
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.18432689 0.25450913 0.10537383
## Lag 2e+05 0.09733650 0.13051342 0.05042974
## Lag 3e+05 0.05560339 0.07731362 0.05593624
## Lag 4e+05 0.04157838 0.05042004 0.01117125
## Lag 5e+05 0.03978150 0.04706249 0.01978982
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.090880752 0.22206856 0.34089213
## Lag 2e+05 0.039175692 0.09218435 0.19118613
## Lag 3e+05 -0.019713648 0.02313659 0.11184681
## Lag 4e+05 -0.005284444 0.03209245 0.07356146
## Lag 5e+05 -0.002237753 0.02429374 0.06347717
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 0.081568745 0.06187755
## Lag 2e+05 0.037379124 0.02533110
## Lag 3e+05 0.044235342 0.01408561
## Lag 4e+05 -0.001269057 -0.02246582
## Lag 5e+05 0.007526589 -0.01685211
## Chain 5
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 0.141859336 0.26323174
## Lag 2e+05 0.067572455 0.11905219
## Lag 3e+05 0.024330710 0.06236396
## Lag 4e+05 0.023038698 0.04353498
## Lag 5e+05 -0.002550583 0.03398363
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.020228218
## Lag 2e+05 0.013309309
## Lag 3e+05 -0.019394764
## Lag 4e+05 0.001783148
## Lag 5e+05 -0.019122919
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.0000000000
## Lag 1e+05 -0.0043788547
## Lag 2e+05 -0.0333826931
## Lag 3e+05 0.0237917936
## Lag 4e+05 0.0206845054
## Lag 5e+05 0.0004209339
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.21928280
## Lag 2e+05 0.09243612
## Lag 3e+05 0.06981057
## Lag 4e+05 0.06043072
## Lag 5e+05 0.01966646
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O3
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.23958489 0.010345456
## Lag 2e+05 0.12212418 -0.018950102
## Lag 3e+05 0.07150297 -0.026391529
## Lag 4e+05 0.03890681 -0.010305284
## Lag 5e+05 0.03108642 -0.008526651
## nodefactor.riskg.O4 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.071499222 0.006874296 -0.0086052387
## Lag 2e+05 0.067636438 -0.018231544 -0.0006179379
## Lag 3e+05 0.008193176 0.005819852 -0.0029209659
## Lag 4e+05 -0.030279549 0.018780463 -0.0030418251
## Lag 5e+05 -0.014652451 0.006202777 -0.0209453603
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.203399210 0.23509182 0.119166830
## Lag 2e+05 0.098170213 0.09172976 0.038646073
## Lag 3e+05 0.037626637 0.06050897 0.027034581
## Lag 4e+05 0.024157256 0.02815295 0.008692159
## Lag 5e+05 0.008478862 0.01713609 0.016708837
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.091360907 0.193397795 0.31265345
## Lag 2e+05 0.045196066 0.096833135 0.18173857
## Lag 3e+05 0.017781998 0.067078224 0.12464086
## Lag 4e+05 0.014631851 0.040215810 0.06180591
## Lag 5e+05 -0.009896387 0.009040489 0.07564116
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.094871119 0.056632284
## Lag 2e+05 0.052254132 0.006637746
## Lag 3e+05 0.001914888 -0.004746090
## Lag 4e+05 0.008480253 0.006960612
## Lag 5e+05 -0.028473151 -0.009671884
## Chain 6
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 0.146429733 0.21389817
## Lag 2e+05 0.050810896 0.09438616
## Lag 3e+05 0.045309384 0.06594349
## Lag 4e+05 -0.003960750 0.01161823
## Lag 5e+05 -0.009377327 -0.02446500
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.040174865
## Lag 2e+05 0.010602099
## Lag 3e+05 0.017583694
## Lag 4e+05 -0.008827474
## Lag 5e+05 -0.004536169
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.007024220
## Lag 2e+05 0.005056349
## Lag 3e+05 0.003087674
## Lag 4e+05 0.012645121
## Lag 5e+05 -0.008496157
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.251318843
## Lag 2e+05 0.104792319
## Lag 3e+05 0.050962205
## Lag 4e+05 0.044092506
## Lag 5e+05 0.007796539
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O3
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.25749837 -0.005326010
## Lag 2e+05 0.06841016 0.004883353
## Lag 3e+05 0.06851989 0.022002242
## Lag 4e+05 0.05096636 -0.010828733
## Lag 5e+05 0.02573452 0.012247320
## nodefactor.riskg.O4 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0 1.0000000000 1.000000000 1.0000000000
## Lag 1e+05 0.0523930200 -0.005850379 0.0105735196
## Lag 2e+05 0.0003982336 0.019825820 -0.0180092752
## Lag 3e+05 -0.0010659390 -0.005030450 0.0135792632
## Lag 4e+05 0.0062253160 0.014016531 -0.0079487735
## Lag 5e+05 -0.0296860952 -0.011752125 0.0006864114
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.162982608 0.24147591 0.140504310
## Lag 2e+05 0.059378500 0.10833951 0.023328230
## Lag 3e+05 0.069113369 0.05060211 0.010315607
## Lag 4e+05 0.019650846 0.07544432 -0.001837097
## Lag 5e+05 -0.004281127 0.02500527 0.004960597
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.102512323 0.174695171 0.33338311
## Lag 2e+05 0.004915070 0.055150142 0.20126168
## Lag 3e+05 0.039342429 0.035862367 0.14309675
## Lag 4e+05 0.006816913 0.009343012 0.10498882
## Lag 5e+05 -0.011874535 -0.017896606 0.06470738
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.114923581 0.049690588
## Lag 2e+05 0.038861994 -0.008758269
## Lag 3e+05 0.044013291 0.010777877
## Lag 4e+05 0.005822066 -0.016233649
## Lag 5e+05 -0.010136372 -0.042618501
## Chain 7
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.118544244 0.204508541
## Lag 2e+05 0.051881431 0.089860929
## Lag 3e+05 0.001344600 0.009537561
## Lag 4e+05 0.002276230 -0.012591380
## Lag 5e+05 -0.008765857 -0.019315805
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.034431567
## Lag 2e+05 0.019497168
## Lag 3e+05 0.002389839
## Lag 4e+05 0.016819319
## Lag 5e+05 -0.002945134
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.005068348
## Lag 2e+05 -0.025865708
## Lag 3e+05 0.018683161
## Lag 4e+05 0.011147150
## Lag 5e+05 0.008275449
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.262099486
## Lag 2e+05 0.121572122
## Lag 3e+05 0.036914817
## Lag 4e+05 0.029432832
## Lag 5e+05 0.007749906
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O3
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.24698483 0.007646356
## Lag 2e+05 0.11865524 -0.023077835
## Lag 3e+05 0.06322231 -0.005040786
## Lag 4e+05 0.01328459 0.022980081
## Lag 5e+05 0.02860493 -0.028055940
## nodefactor.riskg.O4 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.054048202 -0.021275686 0.0037736625
## Lag 2e+05 0.001448386 -0.007334797 0.0060893603
## Lag 3e+05 0.016148792 0.013863992 0.0007264019
## Lag 4e+05 0.023278631 -0.004325026 0.0008084412
## Lag 5e+05 0.034427930 -0.003479063 -0.0298294898
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.185890456 0.27040488 0.108635227
## Lag 2e+05 0.060141745 0.13245346 0.060688079
## Lag 3e+05 0.044191189 0.06017585 0.019598947
## Lag 4e+05 0.009742471 0.02264653 0.007237690
## Lag 5e+05 -0.008693382 0.00795050 -0.006669161
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.089878049 0.23045425 0.34811417
## Lag 2e+05 0.044402545 0.07018193 0.20377833
## Lag 3e+05 0.011334753 0.02135984 0.11541246
## Lag 4e+05 0.011893091 0.02118646 0.09368659
## Lag 5e+05 -0.001125065 0.03303309 0.04292301
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.076897547 0.060218666
## Lag 2e+05 0.049492194 0.054895383
## Lag 3e+05 0.005240154 0.016989139
## Lag 4e+05 -0.004469728 0.009819576
## Lag 5e+05 -0.019643179 -0.013556035
## Chain 8
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.138268010 0.239593281
## Lag 2e+05 0.022475924 0.090380169
## Lag 3e+05 0.005308546 0.039007136
## Lag 4e+05 0.004152257 0.012277097
## Lag 5e+05 -0.011339317 0.004044549
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.029697785
## Lag 2e+05 -0.007892638
## Lag 3e+05 -0.013161139
## Lag 4e+05 0.007404809
## Lag 5e+05 -0.007045731
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.008459629
## Lag 2e+05 -0.011793642
## Lag 3e+05 -0.019359458
## Lag 4e+05 -0.030523540
## Lag 5e+05 -0.008880877
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.255165876
## Lag 2e+05 0.096439385
## Lag 3e+05 0.038479191
## Lag 4e+05 0.021606743
## Lag 5e+05 0.008956545
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O3
## Lag 0 1.0000000000 1.00000000
## Lag 1e+05 0.2196487109 -0.01476530
## Lag 2e+05 0.1029871552 0.01485287
## Lag 3e+05 0.0413873762 -0.03654493
## Lag 4e+05 0.0101747952 -0.01544988
## Lag 5e+05 0.0002585535 0.01454550
## nodefactor.riskg.O4 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.074759885 0.0007311869 -0.009128602
## Lag 2e+05 0.004337738 -0.0056744860 -0.034254973
## Lag 3e+05 0.005329008 0.0050142215 -0.033812328
## Lag 4e+05 0.009358909 -0.0074458668 -0.018881387
## Lag 5e+05 -0.018330017 0.0173467308 -0.021313115
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.000000000
## Lag 1e+05 0.18002439 0.23346523 0.100910380
## Lag 2e+05 0.05251901 0.10796879 0.050405727
## Lag 3e+05 0.04830264 0.07019017 0.008068741
## Lag 4e+05 0.02252127 0.04636433 0.033788532
## Lag 5e+05 0.01100147 0.04440526 0.014610176
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.120743816 0.19881321 0.34260743
## Lag 2e+05 0.027371261 0.04461938 0.19848613
## Lag 3e+05 0.002833790 0.01776230 0.12927442
## Lag 4e+05 -0.005216131 0.02591556 0.08618060
## Lag 5e+05 -0.028888288 -0.01917405 0.03309567
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.103442317 0.090816966
## Lag 2e+05 0.018251543 -0.003436404
## Lag 3e+05 0.011405259 0.016722318
## Lag 4e+05 0.010480095 -0.001170132
## Lag 5e+05 -0.009874996 0.006648497
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -1.00703 -1.13846
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 1.94186 0.15041
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.01473 -1.45567
## nodefactor.riskg.O3 nodefactor.riskg.O4
## -0.89230 -0.11357
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.65680 -0.48752
## nodefactor.race..wa.B nodefactor.race..wa.H
## 1.23998 0.08355
## nodefactor.region.EW nodefactor.region.OW
## 0.85702 0.56615
## nodematch.race..wa.B nodematch.race..wa.H
## 3.04481 0.35948
## nodematch.race..wa.O absdiff.sqrt.age
## -1.82512 0.02325
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.313918345 0.254928502
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.052154439 0.880439423
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.988245157 0.145484061
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.372231237 0.909578519
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.511309510 0.625891500
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.214984151 0.933417308
## nodefactor.region.EW nodefactor.region.OW
## 0.391432115 0.571290263
## nodematch.race..wa.B nodematch.race..wa.H
## 0.002328263 0.719236807
## nodematch.race..wa.O absdiff.sqrt.age
## 0.067982409 0.981450945
## Joint P-value (lower = worse): 7.032381e-05 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 2.21347 1.16938
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.78280 -1.11909
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 1.58766 1.66901
## nodefactor.riskg.O3 nodefactor.riskg.O4
## -1.15736 2.06348
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## -0.58553 0.01410
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.13437 2.81408
## nodefactor.region.EW nodefactor.region.OW
## 0.73337 1.50376
## nodematch.race..wa.B nodematch.race..wa.H
## -0.04712 1.14374
## nodematch.race..wa.O absdiff.sqrt.age
## 0.97446 1.60000
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.026864947 0.242250308
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.433744103 0.263099952
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.112364047 0.095115411
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.247125155 0.039066679
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.558192190 0.988748752
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.893111603 0.004891726
## nodefactor.region.EW nodefactor.region.OW
## 0.463335031 0.132641983
## nodematch.race..wa.B nodematch.race..wa.H
## 0.962414630 0.252729578
## nodematch.race..wa.O absdiff.sqrt.age
## 0.329825996 0.109598646
## Joint P-value (lower = worse): 0.9971656 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.98852 0.03427
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.17830 0.14459
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.35357 2.16301
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.37124 1.78618
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## -1.50018 1.27575
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.25483 0.21451
## nodefactor.region.EW nodefactor.region.OW
## -0.51555 0.67525
## nodematch.race..wa.B nodematch.race..wa.H
## 1.70638 -0.32444
## nodematch.race..wa.O absdiff.sqrt.age
## 1.03737 1.37129
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.32289866 0.97265880
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.85848808 0.88503491
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.72366164 0.03054016
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.71046154 0.07407081
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.13356773 0.20204297
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.79885727 0.83014858
## nodefactor.region.EW nodefactor.region.OW
## 0.60617017 0.49951414
## nodematch.race..wa.B nodematch.race..wa.H
## 0.08793687 0.74560763
## nodematch.race..wa.O absdiff.sqrt.age
## 0.29956179 0.17028567
## Joint P-value (lower = worse): 0.1853672 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.85872 -0.87987
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.36650 0.52669
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.14199 -1.66318
## nodefactor.riskg.O3 nodefactor.riskg.O4
## -0.16610 -0.82847
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 1.17896 -0.01855
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.46441 -0.30425
## nodefactor.region.EW nodefactor.region.OW
## 0.83459 -0.94426
## nodematch.race..wa.B nodematch.race..wa.H
## -1.63266 0.25031
## nodematch.race..wa.O absdiff.sqrt.age
## -0.17568 -0.14602
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.39049258 0.37893188
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.71398851 0.59840886
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.88708917 0.09627569
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.86807669 0.40740503
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.23841455 0.98519772
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.64235553 0.76093607
## nodefactor.region.EW nodefactor.region.OW
## 0.40394759 0.34503561
## nodematch.race..wa.B nodematch.race..wa.H
## 0.10254045 0.80235010
## nodematch.race..wa.O absdiff.sqrt.age
## 0.86054487 0.88390664
## Joint P-value (lower = worse): 5.793033e-34 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -1.33547 -2.04257
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.53342 0.26897
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -2.12001 1.81244
## nodefactor.riskg.O3 nodefactor.riskg.O4
## -1.77704 -0.94025
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## -0.07102 0.21282
## nodefactor.race..wa.B nodefactor.race..wa.H
## -2.29543 -2.34880
## nodefactor.region.EW nodefactor.region.OW
## -1.15958 -1.31951
## nodematch.race..wa.B nodematch.race..wa.H
## -1.73288 -1.57003
## nodematch.race..wa.O absdiff.sqrt.age
## 0.63060 -1.23131
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.18172297 0.04109456
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.59374227 0.78795394
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.03400560 0.06991805
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.07556239 0.34708909
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.94338141 0.83146861
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.02170832 0.01883409
## nodefactor.region.EW nodefactor.region.OW
## 0.24622157 0.18699996
## nodematch.race..wa.B nodematch.race..wa.H
## 0.08311783 0.11640725
## nodematch.race..wa.O absdiff.sqrt.age
## 0.52829947 0.21820755
## Joint P-value (lower = worse): 0.009573772 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.44769 -0.86900
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.45620 0.98582
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.07921 -0.48160
## nodefactor.riskg.O3 nodefactor.riskg.O4
## -1.50487 -1.56308
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## -0.03429 0.02936
## nodefactor.race..wa.B nodefactor.race..wa.H
## -1.94566 -1.89733
## nodefactor.region.EW nodefactor.region.OW
## 0.09207 -1.18132
## nodematch.race..wa.B nodematch.race..wa.H
## -0.85663 -2.74072
## nodematch.race..wa.O absdiff.sqrt.age
## 0.68024 -0.09961
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.654375600 0.384849446
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.648249712 0.324220565
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.936867989 0.630090902
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.132357431 0.118033113
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.972648073 0.976574736
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.051695786 0.057784673
## nodefactor.region.EW nodefactor.region.OW
## 0.926639275 0.237475345
## nodematch.race..wa.B nodematch.race..wa.H
## 0.391649683 0.006130532
## nodematch.race..wa.O absdiff.sqrt.age
## 0.496353263 0.920652530
## Joint P-value (lower = worse): 1 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -1.76903 -0.88626
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -1.03079 -0.49953
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.04141 -0.78590
## nodefactor.riskg.O3 nodefactor.riskg.O4
## -0.26974 -1.51307
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## -0.41882 -1.10155
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.02943 -1.21911
## nodefactor.region.EW nodefactor.region.OW
## 0.02914 -0.82736
## nodematch.race..wa.B nodematch.race..wa.H
## -0.01330 -1.37732
## nodematch.race..wa.O absdiff.sqrt.age
## -1.48806 -2.72102
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.076889028 0.375474956
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.302637339 0.617405988
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.966971986 0.431927180
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.787358332 0.130262286
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.675349104 0.270656008
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.976519523 0.222803030
## nodefactor.region.EW nodefactor.region.OW
## 0.976754899 0.408034735
## nodematch.race..wa.B nodematch.race..wa.H
## 0.989388743 0.168412849
## nodematch.race..wa.O absdiff.sqrt.age
## 0.136734753 0.006508127
## Joint P-value (lower = worse): 1 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.002238 1.202013
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.873707 0.271300
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.245436 -0.265179
## nodefactor.riskg.O3 nodefactor.riskg.O4
## -1.134466 -0.219949
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## -0.503565 1.693236
## nodefactor.race..wa.B nodefactor.race..wa.H
## 1.408517 -0.953453
## nodefactor.region.EW nodefactor.region.OW
## 0.976078 -0.936797
## nodematch.race..wa.B nodematch.race..wa.H
## 0.405580 -0.429576
## nodematch.race..wa.O absdiff.sqrt.age
## 0.048991 0.650336
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.99821438 0.22935845
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.38227764 0.78615993
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.80611860 0.79087128
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.25659924 0.82591098
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.61456728 0.09041063
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.15897815 0.34036083
## nodefactor.region.EW nodefactor.region.OW
## 0.32902570 0.34886295
## nodematch.race..wa.B nodematch.race..wa.H
## 0.68505158 0.66750409
## nodematch.race..wa.O absdiff.sqrt.age
## 0.96092638 0.51547547
## Joint P-value (lower = worse): 0.9840542 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 8
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 3.1536 21.895 0.12641 0.16943
## nodefactor.deg.main.deg.pers.0.1 1.2120 14.221 0.08211 0.12994
## nodefactor.deg.main.deg.pers.0.2 0.5271 6.218 0.03590 0.03783
## nodefactor.deg.main.deg.pers.1.0 0.2732 6.333 0.03657 0.03652
## nodefactor.deg.main.deg.pers.1.1 0.4973 12.387 0.07152 0.11614
## nodefactor.deg.main.deg.pers.1.2 0.5981 12.979 0.07494 0.11871
## nodefactor.riskg.O3 0.1885 2.689 0.01553 0.01557
## nodefactor.riskg.O4 1.1292 11.754 0.06786 0.08113
## nodefactor.riskg.Y2 -0.1241 2.850 0.01646 0.01652
## nodefactor.riskg.Y3 0.3195 8.771 0.05064 0.05149
## nodefactor.race..wa.B 2.5947 9.256 0.05344 0.07808
## nodefactor.race..wa.H 1.3192 13.263 0.07657 0.13088
## nodefactor.region.EW 0.6758 11.288 0.06517 0.12279
## nodefactor.region.OW 0.6762 20.417 0.11788 0.15530
## nodematch.race..wa.B 1.4928 1.997 0.01153 0.01853
## nodematch.race..wa.H 0.3585 3.689 0.02130 0.04467
## nodematch.race..wa.O 0.8681 16.942 0.09782 0.12643
## nodematch.region 2.9910 19.687 0.11367 0.15961
## absdiff.sqrt.age 2.8393 22.476 0.12976 0.14917
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -39.159 -12.1586 2.84138 17.841 46.841
## nodefactor.deg.main.deg.pers.0.1 -26.310 -8.3100 0.68996 10.690 29.690
## nodefactor.deg.main.deg.pers.0.2 -11.371 -3.3710 0.62897 4.629 13.629
## nodefactor.deg.main.deg.pers.1.0 -12.033 -4.0335 -0.03347 3.967 12.967
## nodefactor.deg.main.deg.pers.1.1 -22.538 -7.5379 0.46214 8.462 25.462
## nodefactor.deg.main.deg.pers.1.2 -24.388 -8.3881 0.61188 9.612 26.612
## nodefactor.riskg.O3 -4.856 -1.8558 0.14418 2.144 6.144
## nodefactor.riskg.O4 -21.513 -6.5127 0.48734 8.487 24.487
## nodefactor.riskg.Y2 -5.202 -2.2024 -0.20238 1.798 5.798
## nodefactor.riskg.Y3 -15.786 -5.7860 0.21403 6.214 18.214
## nodefactor.race..wa.B -15.591 -3.5908 2.40918 8.409 21.409
## nodefactor.race..wa.H -24.174 -8.1739 0.82608 9.826 27.826
## nodefactor.region.EW -20.501 -7.5014 0.49862 8.499 23.499
## nodefactor.region.OW -38.486 -13.4862 0.51379 14.514 41.514
## nodematch.race..wa.B -1.540 0.4601 1.46015 2.460 5.460
## nodematch.race..wa.H -6.269 -2.2690 -0.26902 2.731 7.731
## nodematch.race..wa.O -31.880 -10.8800 1.11998 12.120 35.120
## nodematch.region -35.327 -10.3269 2.67310 16.673 42.673
## absdiff.sqrt.age -40.241 -12.4725 2.44631 17.751 48.225
##
##
## Sample statistics cross-correlations:
## edges
## edges 1.00000000
## nodefactor.deg.main.deg.pers.0.1 0.55370244
## nodefactor.deg.main.deg.pers.0.2 0.27156284
## nodefactor.deg.main.deg.pers.1.0 0.27013977
## nodefactor.deg.main.deg.pers.1.1 0.49994536
## nodefactor.deg.main.deg.pers.1.2 0.51075457
## nodefactor.riskg.O3 0.12100448
## nodefactor.riskg.O4 0.42831958
## nodefactor.riskg.Y2 0.12295394
## nodefactor.riskg.Y3 0.36880468
## nodefactor.race..wa.B 0.38323017
## nodefactor.race..wa.H 0.51061101
## nodefactor.region.EW 0.33899606
## nodefactor.region.OW 0.54804058
## nodematch.race..wa.B 0.09331197
## nodematch.race..wa.H 0.16845344
## nodematch.race..wa.O 0.77361592
## nodematch.region 0.89506007
## absdiff.sqrt.age 0.77321735
## nodefactor.deg.main.deg.pers.0.1
## edges 0.55370244
## nodefactor.deg.main.deg.pers.0.1 1.00000000
## nodefactor.deg.main.deg.pers.0.2 0.08056047
## nodefactor.deg.main.deg.pers.1.0 0.06937749
## nodefactor.deg.main.deg.pers.1.1 0.15298314
## nodefactor.deg.main.deg.pers.1.2 0.14180555
## nodefactor.riskg.O3 0.07007030
## nodefactor.riskg.O4 0.23796555
## nodefactor.riskg.Y2 0.06534959
## nodefactor.riskg.Y3 0.18738423
## nodefactor.race..wa.B 0.23851586
## nodefactor.race..wa.H 0.20524282
## nodefactor.region.EW 0.17774931
## nodefactor.region.OW 0.35231048
## nodematch.race..wa.B 0.06115229
## nodematch.race..wa.H 0.04072498
## nodematch.race..wa.O 0.46151575
## nodematch.region 0.48990203
## absdiff.sqrt.age 0.41842794
## nodefactor.deg.main.deg.pers.0.2
## edges 0.27156284
## nodefactor.deg.main.deg.pers.0.1 0.08056047
## nodefactor.deg.main.deg.pers.0.2 1.00000000
## nodefactor.deg.main.deg.pers.1.0 0.03591671
## nodefactor.deg.main.deg.pers.1.1 0.06071575
## nodefactor.deg.main.deg.pers.1.2 0.07556606
## nodefactor.riskg.O3 0.03815016
## nodefactor.riskg.O4 0.12740122
## nodefactor.riskg.Y2 0.03138381
## nodefactor.riskg.Y3 0.10872152
## nodefactor.race..wa.B 0.11933379
## nodefactor.race..wa.H 0.14140374
## nodefactor.region.EW 0.07112511
## nodefactor.region.OW 0.14283431
## nodematch.race..wa.B 0.02271257
## nodematch.race..wa.H 0.04479195
## nodematch.race..wa.O 0.20131042
## nodematch.region 0.25042027
## absdiff.sqrt.age 0.21046671
## nodefactor.deg.main.deg.pers.1.0
## edges 0.27013977
## nodefactor.deg.main.deg.pers.0.1 0.06937749
## nodefactor.deg.main.deg.pers.0.2 0.03591671
## nodefactor.deg.main.deg.pers.1.0 1.00000000
## nodefactor.deg.main.deg.pers.1.1 0.05744992
## nodefactor.deg.main.deg.pers.1.2 0.06919164
## nodefactor.riskg.O3 0.01991066
## nodefactor.riskg.O4 0.11233279
## nodefactor.riskg.Y2 0.04010717
## nodefactor.riskg.Y3 0.09072432
## nodefactor.race..wa.B 0.09695218
## nodefactor.race..wa.H 0.16952089
## nodefactor.region.EW 0.10947877
## nodefactor.region.OW 0.14867228
## nodematch.race..wa.B 0.02600118
## nodematch.race..wa.H 0.06323372
## nodematch.race..wa.O 0.19288659
## nodematch.region 0.23763000
## absdiff.sqrt.age 0.20676726
## nodefactor.deg.main.deg.pers.1.1
## edges 0.49994536
## nodefactor.deg.main.deg.pers.0.1 0.15298314
## nodefactor.deg.main.deg.pers.0.2 0.06071575
## nodefactor.deg.main.deg.pers.1.0 0.05744992
## nodefactor.deg.main.deg.pers.1.1 1.00000000
## nodefactor.deg.main.deg.pers.1.2 0.13071195
## nodefactor.riskg.O3 0.06077506
## nodefactor.riskg.O4 0.19192832
## nodefactor.riskg.Y2 0.06395472
## nodefactor.riskg.Y3 0.18867308
## nodefactor.race..wa.B 0.18118749
## nodefactor.race..wa.H 0.30039627
## nodefactor.region.EW 0.15432897
## nodefactor.region.OW 0.28144497
## nodematch.race..wa.B 0.04403783
## nodematch.race..wa.H 0.11713878
## nodematch.race..wa.O 0.36651092
## nodematch.region 0.45201198
## absdiff.sqrt.age 0.37786817
## nodefactor.deg.main.deg.pers.1.2
## edges 0.51075457
## nodefactor.deg.main.deg.pers.0.1 0.14180555
## nodefactor.deg.main.deg.pers.0.2 0.07556606
## nodefactor.deg.main.deg.pers.1.0 0.06919164
## nodefactor.deg.main.deg.pers.1.1 0.13071195
## nodefactor.deg.main.deg.pers.1.2 1.00000000
## nodefactor.riskg.O3 0.06875248
## nodefactor.riskg.O4 0.21244589
## nodefactor.riskg.Y2 0.06436779
## nodefactor.riskg.Y3 0.19292938
## nodefactor.race..wa.B 0.13174330
## nodefactor.race..wa.H 0.32581196
## nodefactor.region.EW 0.20977002
## nodefactor.region.OW 0.24647164
## nodematch.race..wa.B 0.02045403
## nodematch.race..wa.H 0.13568023
## nodematch.race..wa.O 0.38456028
## nodematch.region 0.45738353
## absdiff.sqrt.age 0.39946393
## nodefactor.riskg.O3 nodefactor.riskg.O4
## edges 0.121004484 0.42831958
## nodefactor.deg.main.deg.pers.0.1 0.070070297 0.23796555
## nodefactor.deg.main.deg.pers.0.2 0.038150156 0.12740122
## nodefactor.deg.main.deg.pers.1.0 0.019910664 0.11233279
## nodefactor.deg.main.deg.pers.1.1 0.060775058 0.19192832
## nodefactor.deg.main.deg.pers.1.2 0.068752484 0.21244589
## nodefactor.riskg.O3 1.000000000 0.05678409
## nodefactor.riskg.O4 0.056784087 1.00000000
## nodefactor.riskg.Y2 0.007949908 0.02951831
## nodefactor.riskg.Y3 0.018030116 0.07407588
## nodefactor.race..wa.B 0.027112596 0.18471979
## nodefactor.race..wa.H 0.059875601 0.27707882
## nodefactor.region.EW 0.041155839 0.14391569
## nodefactor.region.OW 0.065278783 0.23007425
## nodematch.race..wa.B 0.006652550 0.04774873
## nodematch.race..wa.H 0.022496505 0.10994369
## nodematch.race..wa.O 0.103780265 0.28898179
## nodematch.region 0.109452692 0.38575122
## absdiff.sqrt.age 0.117851021 0.43421744
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## edges 0.122953943 0.368804681
## nodefactor.deg.main.deg.pers.0.1 0.065349589 0.187384227
## nodefactor.deg.main.deg.pers.0.2 0.031383812 0.108721520
## nodefactor.deg.main.deg.pers.1.0 0.040107166 0.090724322
## nodefactor.deg.main.deg.pers.1.1 0.063954716 0.188673078
## nodefactor.deg.main.deg.pers.1.2 0.064367785 0.192929378
## nodefactor.riskg.O3 0.007949908 0.018030116
## nodefactor.riskg.O4 0.029518313 0.074075880
## nodefactor.riskg.Y2 1.000000000 0.008122821
## nodefactor.riskg.Y3 0.008122821 1.000000000
## nodefactor.race..wa.B 0.049726484 0.138257148
## nodefactor.race..wa.H 0.067708750 0.188283754
## nodefactor.region.EW 0.041707933 0.114638365
## nodefactor.region.OW 0.064409962 0.194462747
## nodematch.race..wa.B 0.014013461 0.036481957
## nodematch.race..wa.H 0.024584362 0.063627350
## nodematch.race..wa.O 0.091365669 0.287770196
## nodematch.region 0.111649132 0.337661029
## absdiff.sqrt.age 0.092765202 0.275136890
## nodefactor.race..wa.B
## edges 0.3832301677
## nodefactor.deg.main.deg.pers.0.1 0.2385158559
## nodefactor.deg.main.deg.pers.0.2 0.1193337903
## nodefactor.deg.main.deg.pers.1.0 0.0969521797
## nodefactor.deg.main.deg.pers.1.1 0.1811874947
## nodefactor.deg.main.deg.pers.1.2 0.1317433018
## nodefactor.riskg.O3 0.0271125961
## nodefactor.riskg.O4 0.1847197851
## nodefactor.riskg.Y2 0.0497264845
## nodefactor.riskg.Y3 0.1382571478
## nodefactor.race..wa.B 1.0000000000
## nodefactor.race..wa.H 0.1315735461
## nodefactor.region.EW 0.0752231545
## nodefactor.region.OW 0.1311124409
## nodematch.race..wa.B 0.4348823200
## nodematch.race..wa.H 0.0026206719
## nodematch.race..wa.O 0.0007773791
## nodematch.region 0.3531366888
## absdiff.sqrt.age 0.3026103987
## nodefactor.race..wa.H
## edges 0.5106110135
## nodefactor.deg.main.deg.pers.0.1 0.2052428185
## nodefactor.deg.main.deg.pers.0.2 0.1414037418
## nodefactor.deg.main.deg.pers.1.0 0.1695208937
## nodefactor.deg.main.deg.pers.1.1 0.3003962719
## nodefactor.deg.main.deg.pers.1.2 0.3258119627
## nodefactor.riskg.O3 0.0598756008
## nodefactor.riskg.O4 0.2770788168
## nodefactor.riskg.Y2 0.0677087495
## nodefactor.riskg.Y3 0.1882837540
## nodefactor.race..wa.B 0.1315735461
## nodefactor.race..wa.H 1.0000000000
## nodefactor.region.EW 0.2889544439
## nodefactor.region.OW 0.2331999444
## nodematch.race..wa.B -0.0006844696
## nodematch.race..wa.H 0.5541746344
## nodematch.race..wa.O -0.0011505123
## nodematch.region 0.4459691645
## absdiff.sqrt.age 0.4039554065
## nodefactor.region.EW nodefactor.region.OW
## edges 0.33899606 0.54804058
## nodefactor.deg.main.deg.pers.0.1 0.17774931 0.35231048
## nodefactor.deg.main.deg.pers.0.2 0.07112511 0.14283431
## nodefactor.deg.main.deg.pers.1.0 0.10947877 0.14867228
## nodefactor.deg.main.deg.pers.1.1 0.15432897 0.28144497
## nodefactor.deg.main.deg.pers.1.2 0.20977002 0.24647164
## nodefactor.riskg.O3 0.04115584 0.06527878
## nodefactor.riskg.O4 0.14391569 0.23007425
## nodefactor.riskg.Y2 0.04170793 0.06440996
## nodefactor.riskg.Y3 0.11463837 0.19446275
## nodefactor.race..wa.B 0.07522315 0.13111244
## nodefactor.race..wa.H 0.28895444 0.23319994
## nodefactor.region.EW 1.00000000 0.06401049
## nodefactor.region.OW 0.06401049 1.00000000
## nodematch.race..wa.B 0.01493065 0.00763674
## nodematch.race..wa.H 0.14274282 0.06444802
## nodematch.race..wa.O 0.21803140 0.48118974
## nodematch.region 0.18849645 0.43755963
## absdiff.sqrt.age 0.25837813 0.42084015
## nodematch.race..wa.B nodematch.race..wa.H
## edges 0.0933119668 0.168453442
## nodefactor.deg.main.deg.pers.0.1 0.0611522941 0.040724976
## nodefactor.deg.main.deg.pers.0.2 0.0227125716 0.044791953
## nodefactor.deg.main.deg.pers.1.0 0.0260011765 0.063233717
## nodefactor.deg.main.deg.pers.1.1 0.0440378250 0.117138783
## nodefactor.deg.main.deg.pers.1.2 0.0204540334 0.135680234
## nodefactor.riskg.O3 0.0066525503 0.022496505
## nodefactor.riskg.O4 0.0477487322 0.109943692
## nodefactor.riskg.Y2 0.0140134606 0.024584362
## nodefactor.riskg.Y3 0.0364819567 0.063627350
## nodefactor.race..wa.B 0.4348823200 0.002620672
## nodefactor.race..wa.H -0.0006844696 0.554174634
## nodefactor.region.EW 0.0149306507 0.142742818
## nodefactor.region.OW 0.0076367402 0.064448018
## nodematch.race..wa.B 1.0000000000 0.002862252
## nodematch.race..wa.H 0.0028622516 1.000000000
## nodematch.race..wa.O 0.0014841620 0.001376098
## nodematch.region 0.0889099625 0.147582074
## absdiff.sqrt.age 0.0747830746 0.132406205
## nodematch.race..wa.O nodematch.region
## edges 0.7736159246 0.89506007
## nodefactor.deg.main.deg.pers.0.1 0.4615157492 0.48990203
## nodefactor.deg.main.deg.pers.0.2 0.2013104223 0.25042027
## nodefactor.deg.main.deg.pers.1.0 0.1928865884 0.23763000
## nodefactor.deg.main.deg.pers.1.1 0.3665109229 0.45201198
## nodefactor.deg.main.deg.pers.1.2 0.3845602784 0.45738353
## nodefactor.riskg.O3 0.1037802653 0.10945269
## nodefactor.riskg.O4 0.2889817923 0.38575122
## nodefactor.riskg.Y2 0.0913656693 0.11164913
## nodefactor.riskg.Y3 0.2877701963 0.33766103
## nodefactor.race..wa.B 0.0007773791 0.35313669
## nodefactor.race..wa.H -0.0011505123 0.44596916
## nodefactor.region.EW 0.2180313957 0.18849645
## nodefactor.region.OW 0.4811897394 0.43755963
## nodematch.race..wa.B 0.0014841620 0.08890996
## nodematch.race..wa.H 0.0013760978 0.14758207
## nodematch.race..wa.O 1.0000000000 0.69503619
## nodematch.region 0.6950361909 1.00000000
## absdiff.sqrt.age 0.5898710577 0.69051430
## absdiff.sqrt.age
## edges 0.77321735
## nodefactor.deg.main.deg.pers.0.1 0.41842794
## nodefactor.deg.main.deg.pers.0.2 0.21046671
## nodefactor.deg.main.deg.pers.1.0 0.20676726
## nodefactor.deg.main.deg.pers.1.1 0.37786817
## nodefactor.deg.main.deg.pers.1.2 0.39946393
## nodefactor.riskg.O3 0.11785102
## nodefactor.riskg.O4 0.43421744
## nodefactor.riskg.Y2 0.09276520
## nodefactor.riskg.Y3 0.27513689
## nodefactor.race..wa.B 0.30261040
## nodefactor.race..wa.H 0.40395541
## nodefactor.region.EW 0.25837813
## nodefactor.region.OW 0.42084015
## nodematch.race..wa.B 0.07478307
## nodematch.race..wa.H 0.13240620
## nodematch.race..wa.O 0.58987106
## nodematch.region 0.69051430
## absdiff.sqrt.age 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.00000000
## Lag 1e+05 0.19173876 0.29553230
## Lag 2e+05 0.09764222 0.15894063
## Lag 3e+05 0.06965239 0.10107121
## Lag 4e+05 0.06653555 0.06192337
## Lag 5e+05 0.03663031 0.05438959
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.048027179
## Lag 2e+05 0.029519906
## Lag 3e+05 -0.002333819
## Lag 4e+05 -0.001614253
## Lag 5e+05 0.025985235
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.001430527
## Lag 2e+05 -0.019311044
## Lag 3e+05 0.025876945
## Lag 4e+05 0.014666028
## Lag 5e+05 -0.014053158
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.30715457
## Lag 2e+05 0.19926640
## Lag 3e+05 0.11440171
## Lag 4e+05 0.11061899
## Lag 5e+05 0.09614892
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O3
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.31614011 0.022712703
## Lag 2e+05 0.18182030 -0.016561837
## Lag 3e+05 0.10645294 0.006741233
## Lag 4e+05 0.06324103 -0.019158527
## Lag 5e+05 0.04876113 0.002287580
## nodefactor.riskg.O4 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.10527584 0.022250467 0.006422511
## Lag 2e+05 0.05293589 0.001192246 0.013894036
## Lag 3e+05 0.02228738 -0.004239066 0.011560671
## Lag 4e+05 0.03739752 -0.018518769 -0.016067485
## Lag 5e+05 0.01433896 -0.006469774 0.037473621
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.0000000
## Lag 1e+05 0.23434441 0.29262345 0.3630390
## Lag 2e+05 0.11880178 0.17297242 0.2508196
## Lag 3e+05 0.06886543 0.12479512 0.1737696
## Lag 4e+05 0.03624450 0.09512313 0.1734205
## Lag 5e+05 0.01784914 0.07887973 0.1337633
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 1.00000000 1.0000000
## Lag 1e+05 0.19382257 0.29243702 0.4325071
## Lag 2e+05 0.08304416 0.15016691 0.2880899
## Lag 3e+05 0.04397610 0.10343354 0.2059737
## Lag 4e+05 0.04560242 0.05151167 0.1570095
## Lag 5e+05 0.01051452 0.03580837 0.1289713
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.18556074 0.23978831 0.08707554
## Lag 2e+05 0.07595660 0.11197689 0.03491318
## Lag 3e+05 0.03184654 0.07489279 0.03124358
## Lag 4e+05 0.05393081 0.06523810 0.01797504
## Lag 5e+05 0.02431579 0.04055130 -0.01086627
## Chain 2
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.00000000
## Lag 1e+05 0.24013709 0.31650944
## Lag 2e+05 0.08816983 0.16272206
## Lag 3e+05 0.04050508 0.06812468
## Lag 4e+05 0.02721286 0.04244072
## Lag 5e+05 0.01722515 0.02041771
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.056101382
## Lag 2e+05 -0.013599520
## Lag 3e+05 0.003042401
## Lag 4e+05 -0.013861455
## Lag 5e+05 -0.029120060
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.018174734
## Lag 2e+05 -0.002420569
## Lag 3e+05 -0.015810194
## Lag 4e+05 -0.013973599
## Lag 5e+05 -0.017832095
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.31211536
## Lag 2e+05 0.19513698
## Lag 3e+05 0.15124946
## Lag 4e+05 0.08966042
## Lag 5e+05 0.05949677
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O3
## Lag 0 1.00000000 1.000000e+00
## Lag 1e+05 0.35122310 -1.421399e-02
## Lag 2e+05 0.21730889 -1.323882e-02
## Lag 3e+05 0.13411928 -1.600652e-03
## Lag 4e+05 0.07260502 9.647915e-03
## Lag 5e+05 0.06376903 -9.405375e-05
## nodefactor.riskg.O4 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.146890880 0.002053739 0.012526356
## Lag 2e+05 0.036791850 -0.009561977 0.005943480
## Lag 3e+05 0.028211840 -0.011064424 0.005068848
## Lag 4e+05 0.006527617 0.006023085 -0.032046457
## Lag 5e+05 0.005941588 0.014665964 -0.009685792
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.232000300 0.34612357 0.36585928
## Lag 2e+05 0.104468377 0.18691563 0.24018772
## Lag 3e+05 0.047080494 0.13131361 0.16349267
## Lag 4e+05 0.042031934 0.09742653 0.14413398
## Lag 5e+05 0.009844213 0.06305013 0.08755481
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.0000000
## Lag 1e+05 0.188415123 0.33272778 0.4226504
## Lag 2e+05 0.089811508 0.17998147 0.2798680
## Lag 3e+05 0.045601281 0.11428957 0.2058351
## Lag 4e+05 0.033551969 0.07802161 0.1752585
## Lag 5e+05 0.004552246 0.07222909 0.1405604
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.201513106 0.27138778 0.137176313
## Lag 2e+05 0.076098572 0.11411620 0.044725916
## Lag 3e+05 0.042477568 0.05055771 -0.001533659
## Lag 4e+05 0.012301867 0.04043541 0.005694411
## Lag 5e+05 -0.007851541 0.03724182 0.011697148
## Chain 3
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 0.167055911 0.29774640
## Lag 2e+05 0.056885900 0.14723873
## Lag 3e+05 0.034155529 0.10514562
## Lag 4e+05 0.003935844 0.08073595
## Lag 5e+05 0.014611843 0.04627277
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.047492348
## Lag 2e+05 -0.001341421
## Lag 3e+05 0.024165764
## Lag 4e+05 0.016310567
## Lag 5e+05 0.008937241
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.028083388
## Lag 2e+05 -0.001325568
## Lag 3e+05 0.016145545
## Lag 4e+05 0.011657467
## Lag 5e+05 -0.004575688
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.30550365
## Lag 2e+05 0.17403424
## Lag 3e+05 0.11159183
## Lag 4e+05 0.07302904
## Lag 5e+05 0.02456749
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O3
## Lag 0 1.00000000 1.0000000000
## Lag 1e+05 0.29175843 0.0237908708
## Lag 2e+05 0.14041682 0.0146710283
## Lag 3e+05 0.06992443 -0.0009648373
## Lag 4e+05 0.02250793 -0.0181915086
## Lag 5e+05 0.02491789 -0.0022804600
## nodefactor.riskg.O4 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 0.11087539 0.004368934 -0.01594903
## Lag 2e+05 0.03996922 0.003045722 -0.01511376
## Lag 3e+05 0.03472664 -0.016374422 0.03923252
## Lag 4e+05 0.02311444 0.008001105 -0.02257919
## Lag 5e+05 0.01150124 0.003904963 -0.01482413
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.0000000
## Lag 1e+05 0.26323745 0.28357771 0.3388131
## Lag 2e+05 0.12810485 0.17294764 0.2218310
## Lag 3e+05 0.07218972 0.12279377 0.1652505
## Lag 4e+05 0.05362696 0.04717534 0.1342910
## Lag 5e+05 0.02677583 0.03968706 0.0980300
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 1.00000000 1.0000000
## Lag 1e+05 0.18311551 0.31760104 0.3835897
## Lag 2e+05 0.08983810 0.17286552 0.2582052
## Lag 3e+05 0.07715199 0.13275315 0.1788250
## Lag 4e+05 0.01696687 0.10801237 0.1341556
## Lag 5e+05 0.01772260 0.06222814 0.1020846
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.00000000 1.00000000 1.000000e+00
## Lag 1e+05 0.14711613 0.20481585 1.077353e-01
## Lag 2e+05 0.06691739 0.07174836 3.670464e-02
## Lag 3e+05 0.02626206 0.04974480 3.872789e-03
## Lag 4e+05 0.02847253 0.02288884 1.835038e-05
## Lag 5e+05 0.03107474 0.02150171 1.079323e-02
## Chain 4
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.00000000
## Lag 1e+05 0.23848983 0.34026600
## Lag 2e+05 0.10432844 0.19620088
## Lag 3e+05 0.07103457 0.12913131
## Lag 4e+05 0.02129242 0.07949622
## Lag 5e+05 0.03170337 0.03660791
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.070670919
## Lag 2e+05 0.011280757
## Lag 3e+05 -0.005374838
## Lag 4e+05 -0.009358528
## Lag 5e+05 0.014219573
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.00000000
## Lag 1e+05 -0.03253820
## Lag 2e+05 0.02671449
## Lag 3e+05 -0.01494958
## Lag 4e+05 -0.01894379
## Lag 5e+05 -0.01393642
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.29896324
## Lag 2e+05 0.14774922
## Lag 3e+05 0.07874275
## Lag 4e+05 0.06462772
## Lag 5e+05 0.05521713
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O3
## Lag 0 1.00000000 1.0000000000
## Lag 1e+05 0.35165901 0.0192437684
## Lag 2e+05 0.18898394 0.0189553348
## Lag 3e+05 0.12697289 0.0061400039
## Lag 4e+05 0.09810259 0.0299459071
## Lag 5e+05 0.04438754 0.0003961651
## nodefactor.riskg.O4 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.13862250 0.021525643 0.008165880
## Lag 2e+05 0.04664381 0.007766675 0.034060225
## Lag 3e+05 0.01006810 -0.014598958 0.014571928
## Lag 4e+05 0.02134356 -0.024742072 -0.008357306
## Lag 5e+05 0.04132923 0.008331415 -0.017736989
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.26060033 0.32719884 0.31787746
## Lag 2e+05 0.14852526 0.20504506 0.21987186
## Lag 3e+05 0.09448202 0.13616167 0.14821845
## Lag 4e+05 0.02846825 0.09435218 0.11870121
## Lag 5e+05 0.02882033 0.09977177 0.09919947
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 1.00000000 1.0000000
## Lag 1e+05 0.21089720 0.29027284 0.4057907
## Lag 2e+05 0.08801838 0.17367541 0.2853188
## Lag 3e+05 0.06865370 0.11162544 0.2174550
## Lag 4e+05 0.03774244 0.05572229 0.1777266
## Lag 5e+05 0.01935947 0.03446826 0.1447663
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.176405527 0.27851422 0.14049912
## Lag 2e+05 0.053234807 0.11969452 0.01644901
## Lag 3e+05 0.064848138 0.07097314 0.01910794
## Lag 4e+05 0.013324480 0.03985962 0.00116933
## Lag 5e+05 -0.008447623 0.03746660 0.01623205
## Chain 5
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.00000000
## Lag 1e+05 0.20566948 0.31495444
## Lag 2e+05 0.10384990 0.15569789
## Lag 3e+05 0.05015300 0.10174471
## Lag 4e+05 0.05712862 0.07286382
## Lag 5e+05 0.01693187 0.05919131
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.038616175
## Lag 2e+05 0.002989034
## Lag 3e+05 -0.013073347
## Lag 4e+05 -0.016264102
## Lag 5e+05 -0.008131310
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.008789335
## Lag 2e+05 -0.009403613
## Lag 3e+05 0.019982321
## Lag 4e+05 0.034030158
## Lag 5e+05 0.012830858
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.32045740
## Lag 2e+05 0.16736502
## Lag 3e+05 0.12023350
## Lag 4e+05 0.08756943
## Lag 5e+05 0.05567568
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O3
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.30290494 0.021999068
## Lag 2e+05 0.18715978 0.021222256
## Lag 3e+05 0.09815597 0.010657824
## Lag 4e+05 0.06510247 0.006242909
## Lag 5e+05 0.03426485 0.027110192
## nodefactor.riskg.O4 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.112536859 -0.002712048 -0.015431231
## Lag 2e+05 0.058381075 -0.010989468 -0.012217833
## Lag 3e+05 0.035446238 -0.006746428 -0.002602155
## Lag 4e+05 0.029505485 -0.017606747 -0.022881525
## Lag 5e+05 0.005727452 -0.014929236 0.021390992
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.23624068 0.28411393 0.32118134
## Lag 2e+05 0.15285272 0.17421653 0.19717535
## Lag 3e+05 0.07996567 0.09766509 0.14664319
## Lag 4e+05 0.07464796 0.05122368 0.12005002
## Lag 5e+05 0.03922609 0.03565607 0.07876835
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 1.00000000 1.0000000
## Lag 1e+05 0.21901304 0.30205205 0.3638392
## Lag 2e+05 0.12783429 0.20026428 0.2674607
## Lag 3e+05 0.05878452 0.09754058 0.1938920
## Lag 4e+05 0.02692702 0.05560476 0.1409356
## Lag 5e+05 0.02739730 0.05419514 0.1171262
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.00000000 1.00000000 1.000000000
## Lag 1e+05 0.17194060 0.23427055 0.101044403
## Lag 2e+05 0.07970956 0.11410739 0.045290679
## Lag 3e+05 0.04317282 0.06910353 -0.004530258
## Lag 4e+05 0.06081571 0.05359422 0.023934441
## Lag 5e+05 0.02664680 0.03270409 -0.007646500
## Chain 6
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.00000000
## Lag 1e+05 0.21807264 0.30517085
## Lag 2e+05 0.06809007 0.16075375
## Lag 3e+05 0.03386069 0.07500314
## Lag 4e+05 0.01211059 0.06190667
## Lag 5e+05 0.02340494 0.04712570
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.011598562
## Lag 2e+05 -0.018009349
## Lag 3e+05 0.002199201
## Lag 4e+05 0.017585285
## Lag 5e+05 -0.023614227
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.014574761
## Lag 2e+05 0.001018407
## Lag 3e+05 -0.021683112
## Lag 4e+05 0.019938234
## Lag 5e+05 -0.015559804
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.32626780
## Lag 2e+05 0.17600669
## Lag 3e+05 0.11644544
## Lag 4e+05 0.08834934
## Lag 5e+05 0.07125512
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O3
## Lag 0 1.00000000 1.00000000
## Lag 1e+05 0.27660489 -0.01913345
## Lag 2e+05 0.14434605 -0.01214181
## Lag 3e+05 0.09066479 -0.01768335
## Lag 4e+05 0.05012347 0.01875767
## Lag 5e+05 0.06402386 0.03134832
## nodefactor.riskg.O4 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 0.10897351 0.029863138 0.01421481
## Lag 2e+05 0.04622988 -0.008035964 -0.01472431
## Lag 3e+05 0.01666076 0.015392671 -0.01776789
## Lag 4e+05 -0.02220093 0.024678677 0.03085070
## Lag 5e+05 -0.01020881 -0.026993435 0.03078933
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.0000000
## Lag 1e+05 0.21768348 0.32314905 0.3244362
## Lag 2e+05 0.13502397 0.19739069 0.2322363
## Lag 3e+05 0.06213949 0.12782750 0.1764162
## Lag 4e+05 0.02917587 0.09223941 0.1367592
## Lag 5e+05 0.01150740 0.08312174 0.1156429
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 1.00000000 1.0000000
## Lag 1e+05 0.19261417 0.28991438 0.4279893
## Lag 2e+05 0.05340299 0.16781819 0.2791071
## Lag 3e+05 0.03316085 0.10499468 0.2140794
## Lag 4e+05 -0.01003622 0.07518493 0.1570234
## Lag 5e+05 -0.01680307 0.04377855 0.1354038
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.185229827 0.26474794 0.129601106
## Lag 2e+05 0.047630927 0.09883226 0.027676024
## Lag 3e+05 0.018972692 0.07094443 -0.002170784
## Lag 4e+05 0.015069740 0.02512910 -0.026402548
## Lag 5e+05 0.009118408 0.01716262 0.002672108
## Chain 7
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.00000000
## Lag 1e+05 0.21053295 0.28935379
## Lag 2e+05 0.12985034 0.18607002
## Lag 3e+05 0.07635701 0.10507361
## Lag 4e+05 0.03843508 0.06048246
## Lag 5e+05 0.05317309 0.02222478
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.0000000000
## Lag 1e+05 0.0529139444
## Lag 2e+05 0.0006248598
## Lag 3e+05 0.0080384739
## Lag 4e+05 0.0193115500
## Lag 5e+05 -0.0051945322
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.023379080
## Lag 2e+05 -0.018049281
## Lag 3e+05 0.003760484
## Lag 4e+05 -0.002701123
## Lag 5e+05 -0.016996729
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.34078649
## Lag 2e+05 0.20032800
## Lag 3e+05 0.12353586
## Lag 4e+05 0.08492469
## Lag 5e+05 0.06979101
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O3
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.29544234 -0.002285879
## Lag 2e+05 0.12798732 -0.021796407
## Lag 3e+05 0.06559046 -0.021672691
## Lag 4e+05 0.04192819 0.011902197
## Lag 5e+05 0.04720056 0.002780622
## nodefactor.riskg.O4 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.11712551 -0.014906572 0.001602972
## Lag 2e+05 0.04312621 0.004579393 -0.002696719
## Lag 3e+05 0.05755532 0.022158087 -0.034367395
## Lag 4e+05 0.01884851 -0.007412513 -0.018653662
## Lag 5e+05 0.01549958 0.006639043 0.025736874
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.25877839 0.31755880 0.30318575
## Lag 2e+05 0.14509087 0.20732609 0.21680903
## Lag 3e+05 0.10260308 0.13406946 0.11589284
## Lag 4e+05 0.08031877 0.08153897 0.09006354
## Lag 5e+05 0.07004292 0.06144469 0.06507271
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 1.00000000 1.0000000
## Lag 1e+05 0.20286901 0.28783616 0.3894614
## Lag 2e+05 0.12938339 0.16184802 0.2638032
## Lag 3e+05 0.06330016 0.11467185 0.1951961
## Lag 4e+05 0.03561001 0.04655690 0.1534829
## Lag 5e+05 0.02778209 0.04310919 0.1427636
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.18777686 0.25472892 0.11041157
## Lag 2e+05 0.10379200 0.12109043 0.02228423
## Lag 3e+05 0.05361570 0.08461371 0.02450302
## Lag 4e+05 0.03786288 0.05757735 0.01116055
## Lag 5e+05 0.04453575 0.06196534 0.02518580
## Chain 8
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 0.185581036 0.31739797
## Lag 2e+05 0.077977550 0.16719776
## Lag 3e+05 0.024748145 0.06540102
## Lag 4e+05 0.032296108 0.07419181
## Lag 5e+05 0.002647112 0.03218604
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.00000000
## Lag 1e+05 0.06923821
## Lag 2e+05 -0.01183036
## Lag 3e+05 -0.01812645
## Lag 4e+05 -0.01116312
## Lag 5e+05 0.02086398
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.018900330
## Lag 2e+05 0.006295372
## Lag 3e+05 -0.007824448
## Lag 4e+05 0.020727229
## Lag 5e+05 0.012396751
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.28419218
## Lag 2e+05 0.15319129
## Lag 3e+05 0.08485995
## Lag 4e+05 0.05391667
## Lag 5e+05 0.02269593
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O3
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.29643358 -0.014649222
## Lag 2e+05 0.16875898 0.026767136
## Lag 3e+05 0.10402296 0.001718842
## Lag 4e+05 0.08434689 -0.007587217
## Lag 5e+05 0.04243569 0.002874885
## nodefactor.riskg.O4 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0 1.0000000000 1.0000000000 1.000000000
## Lag 1e+05 0.1320293722 -0.0226061187 0.021472663
## Lag 2e+05 0.0729152787 -0.0210728063 0.007104583
## Lag 3e+05 0.0368940939 0.0162694346 -0.030386316
## Lag 4e+05 0.0009066786 -0.0003090838 -0.021831120
## Lag 5e+05 0.0210151312 -0.0013604531 -0.006398078
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.242241160 0.29525000 0.33306958
## Lag 2e+05 0.111036978 0.14825275 0.22021154
## Lag 3e+05 0.072120419 0.09142207 0.16503311
## Lag 4e+05 0.017913761 0.06784109 0.13541977
## Lag 5e+05 0.006255107 0.05099074 0.09481549
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.0000000
## Lag 1e+05 0.170408107 0.31369051 0.3996904
## Lag 2e+05 0.060461160 0.17447864 0.2762852
## Lag 3e+05 0.016891531 0.09871003 0.2257577
## Lag 4e+05 0.020589901 0.05728132 0.1777642
## Lag 5e+05 0.003875981 0.03718662 0.1421868
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 0.14932723 0.233029354 0.08452641
## Lag 2e+05 0.08279835 0.098872764 0.02698716
## Lag 3e+05 0.01461446 0.043403464 0.00185019
## Lag 4e+05 0.04393848 0.034497162 0.01716604
## Lag 5e+05 0.01339102 -0.001569331 -0.02631844
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 2.2852 1.3079
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -1.4247 -0.6634
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 1.5022 1.7586
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.3149 1.7219
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 1.6488 -0.4800
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.7071 0.8093
## nodefactor.region.EW nodefactor.region.OW
## 1.8461 1.8715
## nodematch.race..wa.B nodematch.race..wa.H
## 0.4076 1.6387
## nodematch.race..wa.O nodematch.region
## 2.4444 2.5754
## absdiff.sqrt.age
## 1.9043
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.02230331 0.19092026
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.15424344 0.50710312
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.13303865 0.07865254
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.75283838 0.08508909
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.09919469 0.63119252
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.47951007 0.41831866
## nodefactor.region.EW nodefactor.region.OW
## 0.06488265 0.06127621
## nodematch.race..wa.B nodematch.race..wa.H
## 0.68360114 0.10127644
## nodematch.race..wa.O nodematch.region
## 0.01451130 0.01001342
## absdiff.sqrt.age
## 0.05687414
## Joint P-value (lower = worse): 1 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -1.4434 -0.2992
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.4001 -1.6096
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.1149 -0.8093
## nodefactor.riskg.O3 nodefactor.riskg.O4
## -1.4537 -1.0079
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## -0.3018 0.1093
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.7947 -1.9996
## nodefactor.region.EW nodefactor.region.OW
## -0.7052 -0.5196
## nodematch.race..wa.B nodematch.race..wa.H
## 0.6128 -0.9763
## nodematch.race..wa.O nodematch.region
## -0.9044 -1.4090
## absdiff.sqrt.age
## -1.1199
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.14889633 0.76476875
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.68906554 0.10749467
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.90850888 0.41835324
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.14604048 0.31348387
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.76282719 0.91295328
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.42678026 0.04554003
## nodefactor.region.EW nodefactor.region.OW
## 0.48066381 0.60337717
## nodematch.race..wa.B nodematch.race..wa.H
## 0.53999998 0.32893932
## nodematch.race..wa.O nodematch.region
## 0.36577954 0.15884113
## absdiff.sqrt.age
## 0.26275945
## Joint P-value (lower = worse): 0.9767558 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 1.78639 0.46751
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.53966 -0.05748
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.71256 1.10260
## nodefactor.riskg.O3 nodefactor.riskg.O4
## -0.00734 0.58430
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## -0.62953 -0.91125
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.99426 0.19439
## nodefactor.region.EW nodefactor.region.OW
## -1.03499 1.25258
## nodematch.race..wa.B nodematch.race..wa.H
## 1.70684 -1.42237
## nodematch.race..wa.O nodematch.region
## 1.26924 1.39130
## absdiff.sqrt.age
## 1.25631
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.07403614 0.64013646
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.58943338 0.95416455
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.47611650 0.27020156
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.99414341 0.55902132
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.52900074 0.36216200
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.32009644 0.84587028
## nodefactor.region.EW nodefactor.region.OW
## 0.30067371 0.21036009
## nodematch.race..wa.B nodematch.race..wa.H
## 0.08785088 0.15491860
## nodematch.race..wa.O nodematch.region
## 0.20435479 0.16413472
## absdiff.sqrt.age
## 0.20900338
## Joint P-value (lower = worse): 1 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.835350 -0.404192
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.617472 -1.254774
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -1.856703 0.881783
## nodefactor.riskg.O3 nodefactor.riskg.O4
## -1.108273 -0.002741
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 1.672968 0.357563
## nodefactor.race..wa.B nodefactor.race..wa.H
## 1.019745 -2.196457
## nodefactor.region.EW nodefactor.region.OW
## -2.941851 -1.013437
## nodematch.race..wa.B nodematch.race..wa.H
## 0.888698 -1.004352
## nodematch.race..wa.O nodematch.region
## -0.205062 -0.619324
## absdiff.sqrt.age
## 0.425785
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.403520950 0.686071351
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.536923758 0.209560759
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.063353415 0.377894088
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.267744093 0.997812960
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.094333572 0.720670415
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.307849375 0.028059283
## nodefactor.region.EW nodefactor.region.OW
## 0.003262573 0.310851258
## nodematch.race..wa.B nodematch.race..wa.H
## 0.374165608 0.315208960
## nodematch.race..wa.O nodematch.region
## 0.837523926 0.535703224
## absdiff.sqrt.age
## 0.670264879
## Joint P-value (lower = worse): 0.01113013 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 1.475496 -0.115297
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.145503 -0.063612
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.597234 1.523769
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.006298 0.607736
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## -1.161335 -0.142838
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.107070 1.588233
## nodefactor.region.EW nodefactor.region.OW
## -0.564120 -0.121063
## nodematch.race..wa.B nodematch.race..wa.H
## 0.678465 0.434220
## nodematch.race..wa.O nodematch.region
## 0.396667 1.719448
## absdiff.sqrt.age
## 0.794187
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.14007935 0.90820951
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.88431368 0.94927910
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.55035139 0.12756638
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.99497462 0.54336230
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.24550555 0.88641852
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.91473328 0.11223360
## nodefactor.region.EW nodefactor.region.OW
## 0.57267232 0.90364133
## nodematch.race..wa.B nodematch.race..wa.H
## 0.49747693 0.66412857
## nodematch.race..wa.O nodematch.region
## 0.69161334 0.08553286
## absdiff.sqrt.age
## 0.42708672
## Joint P-value (lower = worse): 0.02803736 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 1.5843 -1.0149
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.1124 1.3303
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 1.9868 1.5478
## nodefactor.riskg.O3 nodefactor.riskg.O4
## -0.1654 -1.4458
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.4109 1.1207
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.5444 0.8648
## nodefactor.region.EW nodefactor.region.OW
## 0.4938 2.2323
## nodematch.race..wa.B nodematch.race..wa.H
## 1.1209 0.0293
## nodematch.race..wa.O nodematch.region
## 1.4192 1.3324
## absdiff.sqrt.age
## 0.6475
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.11312607 0.31015609
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.91048151 0.18341670
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.04694308 0.12167911
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.86866001 0.14822195
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.68115602 0.26239589
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.58616840 0.38717228
## nodefactor.region.EW nodefactor.region.OW
## 0.62142484 0.02559395
## nodematch.race..wa.B nodematch.race..wa.H
## 0.26233690 0.97662704
## nodematch.race..wa.O nodematch.region
## 0.15582694 0.18272423
## absdiff.sqrt.age
## 0.51728849
## Joint P-value (lower = worse): 0.1255361 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.72447 0.69250
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 1.03444 -0.16282
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -1.35677 -0.81141
## nodefactor.riskg.O3 nodefactor.riskg.O4
## -0.32688 0.49076
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## -0.89151 -0.98783
## nodefactor.race..wa.B nodefactor.race..wa.H
## 1.63580 -1.26856
## nodefactor.region.EW nodefactor.region.OW
## -0.52721 -0.41718
## nodematch.race..wa.B nodematch.race..wa.H
## 1.78173 -2.03753
## nodematch.race..wa.O nodematch.region
## -1.03666 0.03752
## absdiff.sqrt.age
## -2.05450
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.46877629 0.48862449
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.30093219 0.87066399
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.17485544 0.41712816
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.74375647 0.62359340
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.37265804 0.32323771
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.10188042 0.20459658
## nodefactor.region.EW nodefactor.region.OW
## 0.59805068 0.67654404
## nodematch.race..wa.B nodematch.race..wa.H
## 0.07479283 0.04159744
## nodematch.race..wa.O nodematch.region
## 0.29989546 0.97006818
## absdiff.sqrt.age
## 0.03992758
## Joint P-value (lower = worse): 0.06662289 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.46496 0.08844
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.23122 -2.02242
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.02396 0.71062
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.49024 -0.21710
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## -0.80488 0.29206
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.05311 -0.09563
## nodefactor.region.EW nodefactor.region.OW
## -0.06525 0.52435
## nodematch.race..wa.B nodematch.race..wa.H
## -0.38021 -0.14537
## nodematch.race..wa.O nodematch.region
## 0.36343 0.22887
## absdiff.sqrt.age
## 0.02275
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.64196240 0.92952591
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.81714423 0.04313297
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.98088792 0.47732203
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.62396446 0.82813159
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.42088973 0.77024296
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.95764349 0.92381699
## nodefactor.region.EW nodefactor.region.OW
## 0.94797541 0.60003712
## nodematch.race..wa.B nodematch.race..wa.H
## 0.70379167 0.88441849
## nodematch.race..wa.O nodematch.region
## 0.71628696 0.81896616
## absdiff.sqrt.age
## 0.98184777
## Joint P-value (lower = worse): 0.992922 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Summary of model fit
Model 1
summary(est.i.buildup.bal[[1]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x556c7b13f3d8>
##
## Iterations: 2 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -11.48559 0.04611 0 <1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 <1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 <1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -InfModel 2
summary(est.i.buildup.bal[[2]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("race..wa", base = 3) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x556c9649a898>
##
## Iterations: 2 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -11.64961 0.05801 0 < 1e-04 ***
## nodefactor.race..wa.B 0.34253 0.12034 0 0.00442 **
## nodefactor.race..wa.H 0.44595 0.08955 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -InfModel 3
summary(est.i.buildup.bal[[3]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("race..wa", base = 3) + nodematch("race..wa",
## diff = TRUE) + offset(nodematch("role.class", diff = TRUE,
## keep = 1:2))
## <environment: 0x556cac2e0988>
##
## Iterations: 2 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -12.1460 0.2978 0 < 1e-04 ***
## nodefactor.race..wa.B 0.7670 0.2638 0 0.00364 **
## nodefactor.race..wa.H 0.8668 0.2806 0 0.00201 **
## nodematch.race..wa.B -0.5082 0.6946 0 0.46439
## nodematch.race..wa.H -0.2128 0.4044 0 0.59882
## nodematch.race..wa.O 0.5180 0.3036 0 0.08797 .
## nodematch.role.class.I -Inf 0.0000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.0000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -InfModel 4
summary(est.i.buildup.bal[[4]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor(c("deg.main", "deg.pers")) + nodefactor("race..wa",
## base = 3) + nodematch("race..wa", diff = TRUE) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x556cc2386c30>
##
## Iterations: 2 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -11.91992 0.30551 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.1 0.80997 0.08953 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.2 -0.82193 0.17285 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.0 -2.24970 0.16908 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.1 0.80905 0.09903 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.2 0.76871 0.09601 0 < 1e-04 ***
## nodefactor.race..wa.B 0.76800 0.26498 0 0.00375 **
## nodefactor.race..wa.H 0.89139 0.28034 0 0.00147 **
## nodematch.race..wa.B -0.51529 0.69568 0 0.45888
## nodematch.race..wa.H -0.21131 0.40417 0 0.60110
## nodematch.race..wa.O 0.52019 0.30285 0 0.08586 .
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -InfModel 5
summary(est.i.buildup.bal[[5]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor(c("deg.main", "deg.pers")) + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + offset(nodematch("role.class", diff = TRUE,
## keep = 1:2))
## <environment: 0x556cd86d05a8>
##
## Iterations: 2 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -11.64816 0.30809 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.1 0.81259 0.08956 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.2 -0.81563 0.17163 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.0 -2.24239 0.16865 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.1 0.81298 0.09851 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.2 0.75928 0.09624 0 < 1e-04 ***
## nodefactor.race..wa.B 0.72389 0.26465 0 0.00623 **
## nodefactor.race..wa.H 0.90240 0.28016 0 0.00128 **
## nodefactor.region.EW -0.28407 0.11808 0 0.01614 *
## nodefactor.region.OW -0.37400 0.07505 0 < 1e-04 ***
## nodematch.race..wa.B -0.51135 0.68682 0 0.45656
## nodematch.race..wa.H -0.21086 0.40040 0 0.59845
## nodematch.race..wa.O 0.51962 0.30260 0 0.08595 .
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -InfModel 6
summary(est.i.buildup.bal[[6]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor(c("deg.main", "deg.pers")) + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + absdiff("sqrt.age") + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x556cef616e80>
##
## Iterations: 2 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -11.03749 0.31295 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.1 0.80724 0.08963 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.2 -0.81455 0.17296 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.0 -2.24844 0.16825 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.1 0.80391 0.09880 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.2 0.76688 0.09632 0 < 1e-04 ***
## nodefactor.race..wa.B 0.73962 0.26273 0 0.00488 **
## nodefactor.race..wa.H 0.90781 0.27994 0 0.00118 **
## nodefactor.region.EW -0.28654 0.11694 0 0.01427 *
## nodefactor.region.OW -0.37857 0.07530 0 < 1e-04 ***
## nodematch.race..wa.B -0.51022 0.68917 0 0.45909
## nodematch.race..wa.H -0.20857 0.40390 0 0.60558
## nodematch.race..wa.O 0.51717 0.30200 0 0.08681 .
## absdiff.sqrt.age -0.63434 0.06817 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -InfModel 7
summary(est.i.buildup.bal[[7]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor(c("deg.main", "deg.pers")) + nodefactor("riskg",
## base = 8) + nodefactor("race..wa", base = 3) + nodefactor("region",
## base = 2) + nodematch("race..wa", diff = TRUE) + absdiff("sqrt.age") +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x556d06781378>
##
## Iterations: 2 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -8.29452 0.31519 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.1 0.85430 0.09032 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.2 -0.80260 0.17194 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.0 -2.29710 0.16895 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.1 0.81692 0.09858 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.2 0.76436 0.09618 0 < 1e-04 ***
## nodefactor.riskg.O1 -Inf 0.00000 0 < 1e-04 ***
## nodefactor.riskg.O2 -Inf 0.00000 0 < 1e-04 ***
## nodefactor.riskg.O3 -3.35098 0.38165 0 < 1e-04 ***
## nodefactor.riskg.O4 -0.49646 0.09688 0 < 1e-04 ***
## nodefactor.riskg.Y1 -Inf 0.00000 0 < 1e-04 ***
## nodefactor.riskg.Y2 -4.57088 0.35213 0 < 1e-04 ***
## nodefactor.riskg.Y3 -2.38162 0.12516 0 < 1e-04 ***
## nodefactor.race..wa.B 0.69617 0.26577 0 0.008806 **
## nodefactor.race..wa.H 1.00140 0.28157 0 0.000376 ***
## nodefactor.region.EW -0.33932 0.11695 0 0.003716 **
## nodefactor.region.OW -0.42416 0.07598 0 < 1e-04 ***
## nodematch.race..wa.B -0.50139 0.67485 0 0.457502
## nodematch.race..wa.H -0.20522 0.40389 0 0.611371
## nodematch.race..wa.O 0.51762 0.30274 0 0.087309 .
## absdiff.sqrt.age -0.58168 0.07146 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## nodefactor.riskg.O1 nodefactor.riskg.O2 nodefactor.riskg.Y1
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -InfModel 8
summary(est.i.buildup.bal[[8]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor(c("deg.main", "deg.pers")) + nodefactor("riskg",
## base = 8) + nodefactor("race..wa", base = 3) + nodefactor("region",
## base = 2) + nodematch("race..wa", diff = TRUE) + nodematch("region",
## diff = FALSE) + absdiff("sqrt.age") + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x556d232e8688>
##
## Iterations: 2 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -9.76545 0.33238 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.1 0.85374 0.09042 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.2 -0.80116 0.17060 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.0 -2.29803 0.16813 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.1 0.81539 0.09900 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.2 0.76475 0.09580 0 < 1e-04 ***
## nodefactor.riskg.O1 -Inf 0.00000 0 < 1e-04 ***
## nodefactor.riskg.O2 -Inf 0.00000 0 < 1e-04 ***
## nodefactor.riskg.O3 -3.35146 0.37521 0 < 1e-04 ***
## nodefactor.riskg.O4 -0.49466 0.09661 0 < 1e-04 ***
## nodefactor.riskg.Y1 -Inf 0.00000 0 < 1e-04 ***
## nodefactor.riskg.Y2 -4.56734 0.35403 0 < 1e-04 ***
## nodefactor.riskg.Y3 -2.38089 0.12353 0 < 1e-04 ***
## nodefactor.race..wa.B 0.70670 0.26505 0 0.00767 **
## nodefactor.race..wa.H 1.01286 0.28083 0 0.00031 ***
## nodefactor.region.EW 0.39892 0.10124 0 < 1e-04 ***
## nodefactor.region.OW -0.04416 0.06108 0 0.46970
## nodematch.race..wa.B -0.49211 0.58378 0 0.39924
## nodematch.race..wa.H -0.25247 0.40274 0 0.53073
## nodematch.race..wa.O 0.52455 0.30237 0 0.08277 .
## nodematch.region 1.74122 0.12080 0 < 1e-04 ***
## absdiff.sqrt.age -0.58090 0.07141 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## nodefactor.riskg.O1 nodefactor.riskg.O2 nodefactor.riskg.Y1
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -InfNetwork diagnostics
Model 1
(dx_inst1 <- netdx(est.i.buildup.bal[[1]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.bal[[8]]$formation, set.control.ergm = control.simulate.ergm(MCMC.interval = 1e+5, MCMC.burnin = 1e+6)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 479.159 478.942 0 21.864
## nodefactor.deg.main.deg.pers.0.1 NA 68.959 NA 8.630
## nodefactor.deg.main.deg.pers.0.2 NA 73.286 NA 8.868
## nodefactor.deg.main.deg.pers.1.0 NA 318.038 NA 20.482
## nodefactor.deg.main.deg.pers.1.1 NA 52.675 NA 7.379
## nodefactor.deg.main.deg.pers.1.2 NA 59.214 NA 7.890
## nodefactor.riskg.O1 NA 55.032 NA 7.645
## nodefactor.riskg.O2 NA 54.500 NA 7.704
## nodefactor.riskg.O3 NA 55.073 NA 7.668
## nodefactor.riskg.O4 NA 54.616 NA 7.524
## nodefactor.riskg.Y1 NA 185.155 NA 14.963
## nodefactor.riskg.Y2 NA 184.723 NA 14.845
## nodefactor.riskg.Y3 NA 184.328 NA 14.883
## nodefactor.race..wa.B NA 58.282 NA 7.867
## nodefactor.race..wa.H NA 103.830 NA 10.747
## nodefactor.region.EW NA 96.303 NA 10.372
## nodefactor.region.OW NA 315.003 NA 20.445
## nodematch.race..wa.B NA 1.776 NA 1.341
## nodematch.race..wa.H NA 5.614 NA 2.386
## nodematch.race..wa.O NA 330.571 NA 18.219
## nodematch.region NA 212.589 NA 14.381
## absdiff.sqrt.age NA 546.206 NA 30.353
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 1 1 0 0
## Pct Edges Diss 1 1 0 0plot(dx_inst1, type="formation")
plot(dx_inst1, type="duration")
plot(dx_inst1, type="dissolution")
Model 2
(dx_inst2 <- netdx(est.i.buildup.bal[[2]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.bal[[8]]$formation, set.control.ergm = control.simulate.ergm(MCMC.interval = 1e+5, MCMC.burnin = 1e+6)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 479.159 478.973 0.000 21.884
## nodefactor.deg.main.deg.pers.0.1 NA 68.037 NA 8.597
## nodefactor.deg.main.deg.pers.0.2 NA 73.537 NA 8.900
## nodefactor.deg.main.deg.pers.1.0 NA 319.829 NA 20.656
## nodefactor.deg.main.deg.pers.1.1 NA 53.478 NA 7.500
## nodefactor.deg.main.deg.pers.1.2 NA 59.681 NA 7.958
## nodefactor.riskg.O1 NA 55.447 NA 7.543
## nodefactor.riskg.O2 NA 54.878 NA 7.546
## nodefactor.riskg.O3 NA 53.903 NA 7.594
## nodefactor.riskg.O4 NA 55.133 NA 7.665
## nodefactor.riskg.Y1 NA 185.527 NA 14.778
## nodefactor.riskg.Y2 NA 185.305 NA 14.948
## nodefactor.riskg.Y3 NA 183.054 NA 14.861
## nodefactor.race..wa.B 75.591 75.529 -0.001 9.002
## nodefactor.race..wa.H 149.174 148.932 -0.002 13.184
## nodefactor.region.EW NA 100.538 NA 10.526
## nodefactor.region.OW NA 311.499 NA 20.040
## nodematch.race..wa.B NA 2.960 NA 1.728
## nodematch.race..wa.H NA 11.528 NA 3.384
## nodematch.race..wa.O NA 280.750 NA 16.636
## nodematch.region NA 211.392 NA 14.588
## absdiff.sqrt.age NA 546.654 NA 30.383
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 1 1 0 0
## Pct Edges Diss 1 1 0 0plot(dx_inst2, type="formation")
plot(dx_inst2, type="duration")
plot(dx_inst2, type="dissolution")
Model 3
(dx_inst3 <- netdx(est.i.buildup.bal[[3]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.bal[[8]]$formation, set.control.ergm = control.simulate.ergm(MCMC.interval = 1e+5, MCMC.burnin = 1e+6)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 479.159 478.741 -0.001 21.607
## nodefactor.deg.main.deg.pers.0.1 NA 68.189 NA 8.564
## nodefactor.deg.main.deg.pers.0.2 NA 73.463 NA 8.900
## nodefactor.deg.main.deg.pers.1.0 NA 319.687 NA 20.499
## nodefactor.deg.main.deg.pers.1.1 NA 53.479 NA 7.374
## nodefactor.deg.main.deg.pers.1.2 NA 59.961 NA 8.037
## nodefactor.riskg.O1 NA 55.349 NA 7.693
## nodefactor.riskg.O2 NA 54.779 NA 7.573
## nodefactor.riskg.O3 NA 53.985 NA 7.552
## nodefactor.riskg.O4 NA 55.005 NA 7.565
## nodefactor.riskg.Y1 NA 185.369 NA 14.828
## nodefactor.riskg.Y2 NA 185.078 NA 14.783
## nodefactor.riskg.Y3 NA 183.099 NA 14.760
## nodefactor.race..wa.B 75.591 75.170 -0.006 8.902
## nodefactor.race..wa.H 149.174 148.307 -0.006 13.172
## nodefactor.region.EW NA 100.468 NA 10.581
## nodefactor.region.OW NA 311.128 NA 19.931
## nodematch.race..wa.B 2.540 2.522 -0.007 1.574
## nodematch.race..wa.H 13.269 13.023 -0.019 3.598
## nodematch.race..wa.O 286.880 287.085 0.001 16.938
## nodematch.region NA 211.313 NA 14.567
## absdiff.sqrt.age NA 546.386 NA 30.294
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 1 1 0 0
## Pct Edges Diss 1 1 0 0plot(dx_inst3, type="formation")
plot(dx_inst3, type="duration")
plot(dx_inst3, type="dissolution")
Model 4
(dx_inst4 <- netdx(est.i.buildup.bal[[4]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.bal[[8]]$formation, set.control.ergm = control.simulate.ergm(MCMC.interval = 1e+5, MCMC.burnin = 1e+6)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 479.159 462.628 -0.034 22.651
## nodefactor.deg.main.deg.pers.0.1 172.310 161.548 -0.062 14.461
## nodefactor.deg.main.deg.pers.0.2 36.371 36.336 -0.001 6.170
## nodefactor.deg.main.deg.pers.1.0 38.033 38.027 0.000 6.331
## nodefactor.deg.main.deg.pers.1.1 135.538 125.975 -0.071 12.340
## nodefactor.deg.main.deg.pers.1.2 145.388 135.961 -0.065 12.986
## nodefactor.riskg.O1 NA 55.547 NA 7.709
## nodefactor.riskg.O2 NA 52.419 NA 7.550
## nodefactor.riskg.O3 NA 54.915 NA 7.720
## nodefactor.riskg.O4 NA 50.661 NA 7.290
## nodefactor.riskg.Y1 NA 177.157 NA 15.012
## nodefactor.riskg.Y2 NA 181.282 NA 14.861
## nodefactor.riskg.Y3 NA 176.444 NA 14.783
## nodefactor.race..wa.B 75.591 71.189 -0.058 8.958
## nodefactor.race..wa.H 149.174 137.012 -0.082 13.159
## nodefactor.region.EW NA 92.564 NA 10.169
## nodefactor.region.OW NA 298.352 NA 20.408
## nodematch.race..wa.B 2.540 2.431 -0.043 1.569
## nodematch.race..wa.H 13.269 11.196 -0.156 3.399
## nodematch.race..wa.O 286.880 282.096 -0.017 17.198
## nodematch.region NA 207.076 NA 14.775
## absdiff.sqrt.age NA 529.015 NA 31.295
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 1 1 0 0
## Pct Edges Diss 1 1 0 0plot(dx_inst4, type="formation")
plot(dx_inst4, type="duration")
plot(dx_inst4, type="dissolution")
Model 5
(dx_inst5 <- netdx(est.i.buildup.bal[[5]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.bal[[8]]$formation, set.control.ergm = control.simulate.ergm(MCMC.interval = 1e+5, MCMC.burnin = 1e+6)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 479.159 459.595 -0.041 22.929
## nodefactor.deg.main.deg.pers.0.1 172.310 159.624 -0.074 14.435
## nodefactor.deg.main.deg.pers.0.2 36.371 36.330 -0.001 6.108
## nodefactor.deg.main.deg.pers.1.0 38.033 38.058 0.001 6.394
## nodefactor.deg.main.deg.pers.1.1 135.538 124.780 -0.079 12.527
## nodefactor.deg.main.deg.pers.1.2 145.388 134.809 -0.073 13.006
## nodefactor.riskg.O1 NA 55.198 NA 7.757
## nodefactor.riskg.O2 NA 52.914 NA 7.573
## nodefactor.riskg.O3 NA 54.757 NA 7.681
## nodefactor.riskg.O4 NA 50.174 NA 7.251
## nodefactor.riskg.Y1 NA 175.215 NA 14.727
## nodefactor.riskg.Y2 NA 180.132 NA 15.067
## nodefactor.riskg.Y3 NA 175.657 NA 14.893
## nodefactor.race..wa.B 75.591 70.350 -0.069 8.916
## nodefactor.race..wa.H 149.174 135.580 -0.091 13.236
## nodefactor.region.EW 83.501 80.465 -0.036 9.587
## nodefactor.region.OW 242.486 237.429 -0.021 17.263
## nodematch.race..wa.B 2.540 2.412 -0.050 1.558
## nodematch.race..wa.H 13.269 10.973 -0.173 3.360
## nodematch.race..wa.O 286.880 280.806 -0.021 17.094
## nodematch.region NA 230.345 NA 15.966
## absdiff.sqrt.age NA 525.807 NA 31.532
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 1 1 0 0
## Pct Edges Diss 1 1 0 0plot(dx_inst5, type="formation")
plot(dx_inst5, type="duration")
plot(dx_inst5, type="dissolution")
Model 6
(dx_inst6 <- netdx(est.i.buildup.bal[[6]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.bal[[8]]$formation, set.control.ergm = control.simulate.ergm(MCMC.interval = 1e+5, MCMC.burnin = 1e+6)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 479.159 451.820 -0.057 23.336
## nodefactor.deg.main.deg.pers.0.1 172.310 155.220 -0.099 14.474
## nodefactor.deg.main.deg.pers.0.2 36.371 36.400 0.001 6.179
## nodefactor.deg.main.deg.pers.1.0 38.033 37.966 -0.002 6.299
## nodefactor.deg.main.deg.pers.1.1 135.538 121.413 -0.104 12.412
## nodefactor.deg.main.deg.pers.1.2 145.388 130.729 -0.101 13.035
## nodefactor.riskg.O1 NA 50.322 NA 7.559
## nodefactor.riskg.O2 NA 48.384 NA 7.377
## nodefactor.riskg.O3 NA 49.771 NA 7.484
## nodefactor.riskg.O4 NA 45.491 NA 7.170
## nodefactor.riskg.Y1 NA 176.298 NA 15.033
## nodefactor.riskg.Y2 NA 180.726 NA 15.283
## nodefactor.riskg.Y3 NA 176.681 NA 15.000
## nodefactor.race..wa.B 75.591 68.972 -0.088 8.845
## nodefactor.race..wa.H 149.174 132.074 -0.115 13.091
## nodefactor.region.EW 83.501 79.407 -0.049 9.346
## nodefactor.region.OW 242.486 234.601 -0.033 17.461
## nodematch.race..wa.B 2.540 2.342 -0.078 1.533
## nodematch.race..wa.H 13.269 10.534 -0.206 3.312
## nodematch.race..wa.O 286.880 276.963 -0.035 17.385
## nodematch.region NA 225.600 NA 16.046
## absdiff.sqrt.age 380.500 368.339 -0.032 22.986
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 1 1 0 0
## Pct Edges Diss 1 1 0 0plot(dx_inst6, type="formation")
plot(dx_inst6, type="duration")
plot(dx_inst6, type="dissolution")
Model 7
(dx_inst7 <- netdx(est.i.buildup.bal[[7]], nsims = 10, nsteps = 1000, ncores = 4, set.control.ergm = control.simulate.ergm(MCMC.interval = 1e+5, MCMC.burnin = 1e+6)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 479.159 237.216 -0.505 32.047
## nodefactor.deg.main.deg.pers.0.1 172.310 65.856 -0.618 12.887
## nodefactor.deg.main.deg.pers.0.2 36.371 26.579 -0.269 5.809
## nodefactor.deg.main.deg.pers.1.0 38.033 36.380 -0.043 6.196
## nodefactor.deg.main.deg.pers.1.1 135.538 51.622 -0.619 10.442
## nodefactor.deg.main.deg.pers.1.2 145.388 56.272 -0.613 11.096
## nodefactor.riskg.O1 NA 0.000 NA 0.000
## nodefactor.riskg.O2 NA 0.000 NA 0.000
## nodefactor.riskg.O3 6.856 6.787 -0.010 2.605
## nodefactor.riskg.O4 109.513 64.520 -0.411 11.540
## nodefactor.riskg.Y1 NA 0.000 NA 0.000
## nodefactor.riskg.Y2 8.202 8.226 0.003 2.865
## nodefactor.riskg.Y3 70.786 64.635 -0.087 8.767
## nodefactor.race..wa.B 75.591 33.804 -0.553 7.440
## nodefactor.race..wa.H 149.174 61.481 -0.588 11.451
## nodefactor.region.EW 83.501 43.220 -0.482 8.423
## nodefactor.region.OW 242.486 133.560 -0.449 19.858
## nodematch.race..wa.B 2.540 1.125 -0.557 1.077
## nodematch.race..wa.H 13.269 4.287 -0.677 2.155
## nodematch.race..wa.O 286.880 152.625 -0.468 21.400
## absdiff.sqrt.age 380.500 214.889 -0.435 29.213
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 1 1 0 0
## Pct Edges Diss 1 1 0 0plot(dx_inst7, type="formation")
plot(dx_inst7, type="duration")
plot(dx_inst7, type="dissolution")
Model 8
(dx_inst8 <- netdx(est.i.buildup.bal[[8]], nsims = 10, nsteps = 1000, ncores = 4, set.control.ergm = control.simulate.ergm(MCMC.interval = 1e+5, MCMC.burnin = 1e+6)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 479.159 212.692 -0.556 29.753
## nodefactor.deg.main.deg.pers.0.1 172.310 58.543 -0.660 11.758
## nodefactor.deg.main.deg.pers.0.2 36.371 23.873 -0.344 5.567
## nodefactor.deg.main.deg.pers.1.0 38.033 34.615 -0.090 6.157
## nodefactor.deg.main.deg.pers.1.1 135.538 45.990 -0.661 9.603
## nodefactor.deg.main.deg.pers.1.2 145.388 50.342 -0.654 10.445
## nodefactor.riskg.O1 NA 0.000 NA 0.000
## nodefactor.riskg.O2 NA 0.000 NA 0.000
## nodefactor.riskg.O3 6.856 6.680 -0.026 2.616
## nodefactor.riskg.O4 109.513 57.231 -0.477 10.782
## nodefactor.riskg.Y1 NA 0.000 NA 0.000
## nodefactor.riskg.Y2 8.202 8.171 -0.004 2.873
## nodefactor.riskg.Y3 70.786 60.666 -0.143 8.865
## nodefactor.race..wa.B 75.591 30.421 -0.598 6.915
## nodefactor.race..wa.H 149.174 54.687 -0.633 10.686
## nodefactor.region.EW 83.501 36.506 -0.563 8.070
## nodefactor.region.OW 242.486 117.689 -0.515 19.219
## nodematch.race..wa.B 2.540 1.046 -0.588 1.034
## nodematch.race..wa.H 13.269 3.799 -0.714 2.039
## nodematch.race..wa.O 286.880 137.155 -0.522 19.958
## nodematch.region 383.327 145.866 -0.619 23.260
## absdiff.sqrt.age 380.500 193.062 -0.493 27.552
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 1 1 0 0
## Pct Edges Diss 1 1 0 0plot(dx_inst8, type="formation")
plot(dx_inst8, type="duration")
plot(dx_inst8, type="dissolution")